The core problem the researchers set out to solve is well known to anyone who has trained a multilayer perceptron. There is no analytical formula that tells a designer what the optimal architecture for a given dataset looks like, so practitioners fall back on rules of thumb, trial and error, or exhaustive grid searches. Even once an architecture is fixed, the standard training procedure, backpropagation of errors, relies on gradient descent, which is vulnerable to getting trapped in local minima of the error surface and can converge painfully slowly on difficult problems. These two weaknesses compound each other: a poorly chosen architecture can make the training landscape even harder to navigate, while a bad training run can make a good architecture look useless.

Neuroevolution, the field that applies evolutionary computation to neural network design, has spent more than three decades trying to break this deadlock. Landmark work such as Stanley and Miikkulainen’s NEAT, which evolved network topologies through augmenting them over generations, demonstrated that architectures themselves can be treated as evolvable objects. Since then, a long line of metaheuristics, including genetic algorithms, particle swarm optimization, grey wolf optimizers, whale optimizers, and artificial bee colonies, has been applied to everything from selecting hidden neurons to tuning connection weights. Reviews of this literature, including Ojha, Abraham, and Snášel’s survey of two decades of metaheuristic design of feedforward networks, note a persistent fragmentation: most methods optimize only one or two components of the network, leaving the rest to human judgment or to a separate training algorithm.

NN-IMODE takes aim at that fragmentation. It is built on differential evolution, a population-based optimizer introduced by Storn and Price in 1997 that evolves candidate solutions by generating mutant vectors from weighted differences of existing population members and crossing them with parent solutions. What makes the new approach distinctive is its improved multi-operator structure, drawing on the IMODE framework developed by Karam M. Sallam and colleagues at the 2020 IEEE Congress on Evolutionary Computation. Rather than committing to a single mutation strategy, the algorithm deploys several operators in parallel, each maintaining its own subpopulation, and lets the search process itself determine which operators earn a larger share of the population. This division of labor allows the algorithm to balance broad exploration of the search space against fine-grained exploitation of promising regions, a balance that the authors have studied extensively in their earlier work on metaheuristic behavior.

The technical heart of the method is a novel solution representation. Because NN-IMODE must encode discrete architectural choices, such as the number of hidden layers and the count of neurons in each layer, alongside continuous parameters, such as real-valued weights and biases, and categorical choices, such as which activation function each layer applies, a conventional fixed-length numeric vector will not suffice. The researchers designed an encoding that captures all of these elements within a single evolvable structure, so that a differential evolution operator acting on two candidate networks can blend not only their weights but also their architectural genes. This means that a network with two hidden layers and sigmoid activations can, in effect, breed with a network that has three hidden layers and rectified linear units, producing offspring that inherit and recombine traits from both parents.

Evaluating such a method requires a demanding experimental protocol, and the team put NN-IMODE through one of the more comprehensive benchmarks in this literature. The algorithm was tested on 21 benchmark datasets drawn from the UCI Machine Learning Repository, a standard public collection spanning domains from medical diagnosis to image recognition. The comparison set was formidable: Levenberg-Marquardt, the second-order gradient method that remains a workhorse for small multilayer perceptrons; Scaled Conjugate Gradient, another fast gradient-based trainer; Genetic Algorithms; Differential Evolution in its classic form; and hybrid combinations of these methods. Across this gauntlet, NN-IMODE achieved the highest classification accuracy and the lowest test error rates on 18 of the 21 datasets, a result the authors describe as establishing the method as a transformative approach in neuroevolution.

The significance of beating Levenberg-Marquardt and Scaled Conjugate Gradient deserves unpacking. Gradient-based trainers are extremely efficient when they work, but their performance is hostage to the error surface of the architecture they are given. By searching architectures and parameters jointly, NN-IMODE effectively reshapes the landscape it is searching, discarding architectures whose error surfaces are pathological and concentrating its evolutionary effort on network shapes that train well. The multi-operator mechanism adds a second layer of robustness, because no single mutation rule has to succeed on every dataset; operators that happen to suit a given problem’s structure are automatically amplified. This adaptivity is likely what allows the method to generalize across such a heterogeneous collection of datasets rather than excelling only on problems of one particular type.

The work also arrives at a moment of renewed interest in automated neural network design. Neural architecture search has produced striking results in deep learning, but its most celebrated successes rely on enormous computational budgets that put them out of reach for most laboratories. Metaheuristic approaches such as NN-IMODE occupy a complementary niche: they target feedforward networks of the kind used for tabular classification and regression, where a compact, well-tuned multilayer perceptron can still match or beat far larger models, and where the cost of an evolutionary search is measured in hours rather than weeks. The authors’ broader research program, which includes comparative studies of exploration-exploitation balance, initialization methods, chaos-enhanced metaheuristics, and pruning strategies for convolutional networks, positions this paper as the culmination of a systematic effort to understand how evolutionary search behaves on machine learning design problems.

Several caveats temper the enthusiasm. The benchmark datasets, while diverse, are mostly small to medium in scale, and the computational cost of evaluating a population of candidate networks, each of which must be trained and tested, remains substantial compared with a single gradient descent run. The study also reports no external funding, and the authors declare no competing interests, which speaks to its independence but also to the limits of the experimental scale. Whether the multi-operator machinery retains its advantage on very high-dimensional problems, or on architectures with many more layers than typical feedforward networks, is a question for future work. The authors suggest that the method paves the way for broader applications across artificial intelligence and machine learning, and the generality of the encoding scheme gives that claim some plausibility.

Still, the headline result is hard to dismiss. A single algorithm that decides how deep a network should be, how wide, which nonlinearities its neurons should apply, and what every weight and bias should be, and that does so well enough to outperform established training methods on most of two dozen datasets, is a meaningful step toward neural networks that design themselves. For decades, the separation between architecture design and parameter training has been treated as a natural division of labor between human engineers and learning algorithms. NN-IMODE suggests that the division is not a necessity but a habit, and that evolution, given the right representation and enough operators to work with, can handle the whole job at once. As automated machine learning continues its advance, methods of this kind may become the default way that everyday predictive models are built, with human designers specifying only the goal and the data.

Subject of Research: Simultaneous optimization of feedforward neural network architectures and parameters using an improved multi-operator differential evolution algorithm

Article Title: NN-IMODE: a novel approach for simultaneous optimization of architecture and parameters in feedforward neural networks

Article References: Ferhat, A., Zitouni, F., Sallam, K. M., Harous, S., Lakbichi, R., & Limane, A. (2026). NN-IMODE: a novel approach for simultaneous optimization of architecture and parameters in feedforward neural networks. Cluster Computing, 29(13), Article 756. https://doi.org/10.1007/s10586-026-06551-6

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06551-6

Keywords: feedforward neural networks, differential evolution, neuroevolution, metaheuristics, neural network optimization, evolutionary computation, classification accuracy, multi-operator algorithms, UCI benchmark datasets, automated machine learning, backpropagation, architecture search

Feature selection sits at the heart of practical machine learning. Modern datasets can carry hundreds or thousands of candidate variables, ranging from sensor readings and network traffic statistics to gene expression levels. Many of those variables are irrelevant or redundant, and including them inflates computation time, increases memory demands, and can actively degrade model performance by encouraging overfitting. The goal of feature selection is to find the smallest subset of features that still allows a classifier to make accurate predictions. Mathematically, this is a combinatorial optimization problem: with n candidate features, there are 2^n possible subsets, a search space so vast that exhaustive evaluation becomes impossible even for moderately sized datasets.

This is where metaheuristics come in. These are stochastic search strategies, often inspired by natural processes, that explore large search spaces without guaranteeing a perfect solution but usually finding very good ones in reasonable time. The Puma Optimization Algorithm, first proposed in 2024, mimics the cooperative hunting strategies and social behavior of pumas, balancing exploration of new territory with exploitation of promising regions. It was originally designed for continuous optimization problems, where solutions are expressed as real-valued vectors. Feature selection, however, is inherently binary: each feature is either selected or not selected, represented as a one or a zero in a solution vector.

Bridging that gap is the central technical contribution of the new study. The researchers adapted POA to binary search spaces by integrating ten different transfer functions, mathematical mappings that convert the continuous positions generated by the puma-inspired update rules into probabilities of selecting or discarding each feature. Transfer functions come in several families, including sigmoid-based, hyperbolic tangent-based, and newer V-shaped and U-shaped forms, each with different characteristics regarding how aggressively they push probabilities toward zero or one. The choice of transfer function can dramatically affect how thoroughly the algorithm explores the binary space and how quickly it converges, which is why the authors systematically evaluated all ten rather than picking one arbitrarily.

The evaluation unfolded in three stages. First, the team identified which transfer function suited the binary POA best, with the Threshold Cut-off variant, abbreviated BPOATC, emerging as the strongest performer at 91.53 percent classification accuracy. Second, they benchmarked the algorithm against conventional feature selection methods, the classical filter and wrapper techniques that have long served as baseline tools in the field. Third, they compared it against leading metaheuristic competitors, including the Binary Slime Mould Algorithm, Binary Harris Hawks Optimization, Binary Flood Algorithm, Binary Chinese Pangolin Optimizer, and Binary Rüppell’s Fox Optimizer. Each of these rivals draws on a different natural metaphor, from the foraging networks of slime moulds to the cooperative ambush tactics of hawks, and each has been shown effective on feature selection in recent literature, making the comparison a demanding test.

Performance was measured across four criteria: classification accuracy, fitness value, the number of selected features, and computational time. The fitness function used in wrapper-style feature selection typically balances two competing objectives, rewarding high accuracy while penalizing large feature subsets, so a good algorithm must find solutions that are simultaneously accurate and compact. K-Nearest Neighbour served as the primary classifier during the search, with Support Vector Machine, Gaussian Naïve Bayes, and Logistic Regression used in further analysis to verify that the selected feature subsets generalize across different learning algorithms rather than being tuned to a single classifier’s quirks.

The results were consistent. BPOATC demonstrated superior performance compared with both traditional feature selection techniques and the state-of-the-art metaheuristics it was tested against. To guard against the possibility that observed differences were due to random variation, the authors applied the Friedman test, a non-parametric statistical procedure commonly used to rank multiple algorithms across multiple datasets and problem instances. The test outcomes confirmed the statistical superiority of the new algorithm, meaning its advantage over the competitors was unlikely to be a fluke of particular runs or datasets.

Perhaps the most practically significant part of the study is its validation on three real-world Intrusion Detection System datasets: NSL-KDD, UNSW-NB15, and UAV-IDS-2020. Intrusion detection is a domain where feature selection has immediate security value. Network traffic and drone telemetry data contain enormous numbers of features, and detection systems must classify traffic as benign or malicious in near real time. Fewer features mean faster inference, lower memory footprints on edge devices, and reduced exposure to noisy or manipulated inputs. The binary puma algorithm attained leading performance on UNSW-NB15 and UAV-IDS-2020 and near-optimal results on NSL-KDD, demonstrating that its advantages hold up on high-dimensional, security-critical data rather than only on curated benchmark repositories.

The work fits into a broader and rapidly accelerating trend. The past few years have seen a wave of nature-inspired optimizers adapted for feature selection, including algorithms modeled on dung beetles, gray wolves, plant rhizomes, fairy-wrens, chimps, and pangolins, alongside hybrid and multi-objective approaches that treat the accuracy-versus-subset-size trade-off more explicitly. Researchers have also applied these methods to demanding domains such as cancer gene expression data, microarray analysis, diabetes prediction, botnet detection, and wireless sensor networks. The proliferation reflects a genuine need: as datasets grow in dimensionality across science, medicine, and cybersecurity, the ability to automatically distill the informative signal from a sea of variables becomes a bottleneck for the entire machine learning pipeline.

For practitioners, the study offers a concrete new option in the feature selection toolbox, one whose puma-inspired dynamics appear to strike an effective balance between exploring the binary search space and exploiting the best solutions found so far. The datasets used in the evaluation are publicly available, and the authors report no competing interests and no external funding for the research. As machine learning systems are deployed in increasingly resource-constrained and security-sensitive settings, from drones detecting attacks to models screening medical data, algorithms that can automatically identify the few features that truly matter are likely to grow only more valuable. The binary puma, at least on the evidence of this study, is a formidable hunter in that search space.

Subject of Research: A binary puma-inspired metaheuristic algorithm for feature selection in high-dimensional machine learning classification and intrusion detection

Article Title: A novel binary puma optimization algorithm for feature selection problem

Article References: Musawi, S. Z., Farshi, M., & Ebrahimi Mood, S. (2026). A novel binary puma optimization algorithm for feature selection problem. Cluster Computing, 29(13), Article 755. https://doi.org/10.1007/s10586-026-06610-y

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06610-y

Keywords: feature selection, Puma Optimization Algorithm, metaheuristics, machine learning, binary optimization, transfer functions, intrusion detection, classification accuracy, K-Nearest Neighbour, Friedman test, NSL-KDD, UNSW-NB15

Palash Dutta of the Department of Mathematics at Dibrugarh University in Assam, India, conducted a systematic comparison between Diophantine fuzzy models and a far simpler framework known as hyperbolic fuzzy sets. The Diophantine family includes several prominent variants that have attracted considerable attention in multi-criteria decision-making literature: linear Diophantine fuzzy sets, q-rung linear Diophantine fuzzy sets, generalized Diophantine fuzzy sets, and their fractional extensions. These models share a common design philosophy, embedding multiple reference parameters that users can tune to shape how membership and non-membership information is combined. On paper, that flexibility sounds like a strength. In practice, according to the new findings, it may be the very thing that undermines them.

The core problem Dutta set out to test is a trade-off that has received surprisingly little rigorous scrutiny. Every additional reference parameter in a fuzzy model increases its representational flexibility, but it also opens the door to what the study calls parameter-induced distortion. When users must choose parameter values, the model’s decision boundaries shift depending on those choices, and different users, or the same user on different occasions, can arrive at different conclusions from identical data. In high-stakes applications such as medical diagnosis, renewable energy selection, or infrastructure contracting, where fuzzy methods are routinely deployed, that kind of instability is not a theoretical curiosity. It is a practical liability.

To quantify the problem, the study employed two complementary evaluation strategies. The first was a threshold-based binary classification experiment across an expanded dataset of sixty cases, a sample size determined in advance through a priori power analysis rather than chosen after the fact. Crucially, the experiment used dual cut-offs, deliberately exposing how sensitive each model’s classifications were to the parameters embedded in its formulation. The second strategy was a direct assessment of computational complexity, measuring how each framework’s mathematical machinery scales as problem size grows. Together, the two approaches were designed to answer a deceptively simple question: does the extra flexibility of Diophantine models buy anything that matters?

The answer, at least within this experimental design, was an emphatic no. Hyperbolic fuzzy sets, which contain no reference parameters at all, achieved one hundred percent classification accuracy and perfect self-consistency across the full range of threshold values tested in a continuous, full-spectrum sensitivity analysis. The Diophantine variants told a very different story. Plagued by parameter-induced over-prediction and under-prediction, their accuracies collapsed to a range of roughly 43 to 55 percent. The best performer among them, the generalized Diophantine fuzzy set framework, still managed only 68.33 percent. In classification terms, several of the parameterized models performed worse than a coin flip, a result that raises uncomfortable questions about published applications relying on them.

The magnitude of the gap was not merely statistically detectable but practically enormous. Effect size analysis using Cohen’s d yielded values exceeding 3.0 for every comparison between the hyperbolic framework and its Diophantine rivals, a threshold far beyond the conventional benchmarks for large effects in the behavioral and computational sciences. In plain terms, the difference between the two families of models is not a marginal improvement that might vanish under slightly different conditions. It is a chasm, and the study attributes it directly to a structural feature: the parameter-free multiplicative formulation of hyperbolic fuzzy sets, which captures interaction effects transparently without the parametric sensitivity that warps Diophantine decision boundaries.

Computational results reinforced the picture. The hyperbolic framework maintains a lean linear, or O(N), complexity, meaning its cost grows in direct proportion to the size of the problem. The parameter-heavy Diophantine alternatives, by contrast, carry substantial computational overhead stemming from the extra operations their reference parameters require. For small illustrative examples in academic papers, that overhead is easy to overlook. But fuzzy decision models are increasingly proposed for large-scale, real-world systems, from agriculture decision support platforms to construction contract evaluation and solar panel prioritization, and at scale, the difference between linear scaling and parameter-burdened computation becomes consequential.

The study is careful to define its terms, and those definitions sharpen the critique. Sensitivity, in this context, means responsiveness to parameter variations, and the hyperbolic framework exhibits zero sensitivity while Diophantine models are highly sensitive. Subjectivity means dependence on user-defined choices, which the parameter-free approach eliminates entirely. Stability refers to consistency across conditions, and here the contrast is between perfect stability for the hyperbolic model and narrow optimal windows for its rivals, meaning Diophantine models perform acceptably only within limited ranges of parameter settings. Even the celebrated unity constraint, the bounding condition on membership and non-membership grades that Diophantine models relax in the name of generality, turns out to impose only a trivial, non-restrictive burden on the hyperbolic formulation while its relaxation introduces distortion elsewhere.

None of this means the Diophantine research program is without value. These frameworks emerged from a genuine mathematical lineage stretching from Atanassov’s intuitionistic fuzzy sets through Yager’s orthopair generalizations, Pythagorean and Fermatean variants, and a flourishing ecosystem of rung-based extensions. Each generation promised a wider space of admissible membership grades, and each attracted a wave of applications. What the new study challenges is the assumption that wider admissible spaces and more tunable parameters translate into better decisions. Flexibility, the findings suggest, is only a virtue if the person wielding it knows precisely how to wield it, and even then, the model’s output may say more about the chosen parameters than about the underlying problem.

The implications reach well beyond one corner of fuzzy mathematics. Multi-criteria decision-making methods built on parameterized fuzzy sets are embedded in software used for site selection, supplier evaluation, disease diagnosis, and disaster response planning. If the classification reliability of the underlying model can swing by tens of percentage points depending on parameter choices, then the credibility of those applications deserves fresh scrutiny. The study’s message is a bracing one for a field that has rewarded novelty and generality: sometimes the mathematically robust, globally scalable, and highly reliable choice is the one with fewer knobs to turn, and the burden of proof now lies with the complex models to demonstrate that their flexibility earns its keep.

Subject of Research: Comparative reliability and computational complexity of Diophantine fuzzy models versus hyperbolic fuzzy sets in decision-making

Article Title: A critical survey on critical issues of diophantine fuzzy models in light of hyperbolic fuzzy sets

Article References: Dutta, P. (2026). A critical survey on critical issues of diophantine fuzzy models in light of hyperbolic fuzzy sets. International Journal of Data Science and Analytics, 22(1), Article 327. https://doi.org/10.1007/s41060-026-01311-6

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01311-6

Keywords: fuzzy sets, Diophantine fuzzy sets, hyperbolic fuzzy sets, multi-criteria decision-making, classification accuracy, sensitivity analysis, computational complexity, scalability, parameter sensitivity, uncertainty modeling, decision stability, effect size

The method, called FS-GSR—Feature Selection with Group Streaming via Graph-based Scoring and Sparse Reconstruction—was developed by Tianyuan Jia of The Hong Kong Polytechnic University. It tackles a core weakness of existing streaming feature selection techniques: most process features one at a time, ignoring the structural relationships and redundancies that exist both within and between groups of features. When features arrive in batches, as they do in multi-view learning systems or modular sensor deployments, sequential individual processing can disrupt group structures and degrade performance. FS-GSR instead operates at the group level, updating the selected feature subset in real time as each new batch arrives.

At the heart of the approach is a three-stage framework that functions like a computational funnel. The first stage performs a hybrid, graph-guided filtering of each incoming feature group. The algorithm constructs a similarity graph over the samples, connecting points that are close in the feature space, and computes a Laplacian Score for every feature. This score measures how smoothly a feature varies across locally similar samples—features that preserve the intrinsic geometric structure of the data receive low scores, while noisy, irregular features are penalized. In parallel, a supervised F-score evaluates each feature’s discriminative power by comparing between-class variance to within-class variance. The two measures are blended with equal weight, and only the top fraction of features by combined score survives this initial screening.

The second stage refines this candidate pool by preserving structure at a finer granularity. The algorithm performs a spectral decomposition of the graph Laplacian, extracting ten eigenvectors that form a low-dimensional embedding of the sample manifold. Each surviving feature is then evaluated by how well it can reconstruct this structural representation, using a closed-form projection solution that avoids costly iterative optimization. Features that best approximate the manifold’s skeleton are retained, ensuring that the selected subset captures not just individual relevance but the collective geometry of the data. Because the reconstruction coefficients have an explicit analytical solution, this stage adds minimal computational overhead.

The third and final stage is where FS-GSR departs most sharply from its predecessors. All candidate features accumulated from the first two stages are merged into a global pool, and a multi-task Lasso regression is applied across the entire set simultaneously, using one-hot encoded class labels as the response. The L2,1-norm penalty drives the coefficients of redundant features to exactly zero, yielding a compact, non-overlapping final feature set. The authors also provide theoretical backing for this stage, formulating it as a row-support recovery problem in high-dimensional multivariate regression. Under standard conditions on the covariance structure, they prove that the sparse reconstruction can recover the true set of informative features with high probability, provided the effective sample complexity exceeds a specific threshold.

The computational design is notable for its scalability. By confining expensive cubic operations to the fixed sample space rather than the growing feature dimension, the algorithm’s cost per streaming group scales linearly with the number of incoming features. Because the first two stages aggressively compress each group before global optimization, the feature pool fed into the multi-task Lasso remains small, preventing the exponential blow-up that plagues conventional structure-preserving methods. Scalability experiments on the Ovarian Cancer dataset confirmed near-linear growth in running time as feature dimension increased, and revealed a V-shaped trade-off in execution time as the number of streaming groups varied—very few large groups incur heavy intra-group matrix operations, while very many small groups accumulate loop overhead.

Empirically, the method was tested on five benchmark datasets spanning biomedical, image, and text domains, including Colon (62 samples, 2,000 features), Ovarian Cancer (253 samples, 15,154 features), MLL, COIL20, and PCMAC. These datasets deliberately covered contrasting correlation structures: the Ovarian Cancer data exhibited strong intra-group correlations of up to 0.88, while the PCMAC text dataset showed extremely sparse correlations of roughly 0.025. Against four established baselines—OGFS, Group-SAOLA, Fast-OSFS, and alpha-investing—FS-GSR consistently achieved the highest or near-highest classification accuracy and F1 scores under both KNN and SVM classifiers. On the Ovarian Cancer dataset, it reached accuracy and F1 values above 0.99.

Perhaps the most striking result concerned feature compression. In one experimental configuration, FS-GSR retained an average of just 8.12 features from the 15,154-dimensional Ovarian Cancer dataset, while simultaneously improving KNN accuracy from 0.9845 to 0.9960 and SVM accuracy from 0.9885 to 0.9980—a 63 percent compression relative to the two-stage variant alone. Ablation studies confirmed that each stage contributes distinct benefits: the first enables efficient local filtering, the second improves structural consistency, and the third enforces global sparsity and discriminative refinement. The authors note that the degree of compression adapts to each dataset’s intrinsic correlation structure, with strongly correlated data requiring more retained features to preserve associative signals.

Parameter sensitivity analyses reinforced the method’s practicality. Classification accuracy remained stable across broad plateaus for the retention ratios governing the first two stages, and the mixing weight balancing structural and discriminative scoring performed robustly between 0.2 and 0.8, peaking near 0.5. The embedding dimension saturated quickly, with accuracy plateauing once it exceeded five, justifying the default setting of ten. This robustness means practitioners can deploy the method without exhaustive grid searches, using a simple boundary-driven tuning strategy on validation data.

The implications extend well beyond the benchmark datasets. Streaming, group-wise feature arrival is characteristic of stepwise sensor deployment, progressive module activation in multi-view systems, and sequential extraction pipelines in bioinformatics and text mining. By combining graph-based manifold learning with sparse global reconstruction in an online framework, FS-GSR offers a template for learning systems that must remain both accurate and parsimonious as data evolves. The author identifies future extensions toward multi-label settings, incomplete data systems, and broader generalization, suggesting that the challenge of features that never stop flowing is one the field is only beginning to master.

Subject of Research: Group streaming feature selection for high-dimensional data using graph-based scoring and sparse reconstruction

Article Title: FS-GSR: Graph-based scoring and sparse reconstruction for group streaming feature selection

Article References: Jia, T. (2026). FS-GSR: Graph-based scoring and sparse reconstruction for group streaming feature selection. Applied Intelligence, 56(15), Article 445. https://doi.org/10.1007/s10489-026-07498-2

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07498-2

Keywords: feature selection, streaming features, graph-based scoring, sparse reconstruction, multi-task Lasso, Laplacian score, machine learning, high-dimensional data, data mining, classification accuracy, dimensionality reduction, Applied Intelligence