article · Neural Computing and Applications
Feature selection prepares high-dimensional data for machine learning by removing redundant and irrelevant features, mitigating the curse of dimensionality. Because this is a complex combinatorial problem, researchers use meta-heuristic techniques. The standard Sparrow Search Algorithm often struggles with exploration, exploitation trade-offs, and search stagnation. To address these issues, two enhancements were integrated: Random Re-positioning of Roaming Agents and a new Local Search Algorithm. This produced an improved binary variant called iBSSA, tested alongside nine transfer functions to select optimal feature subsets while maximising classification accuracy. Evaluations were conducted on 18 benchmark datasets from the UCI repository using three classifiers: k-Nearest Neighbour, Support Vector Machine, and Random Forest. Compared against twelve established binary meta-heuristic algorithms, iBSSA demonstrated superior performance, reducing feature set sizes by up to 92 percent while attaining up to 100 percent classification accuracy on certain datasets.
High-dimensional datasets can overwhelm machine learning systems with irrelevant information, slowing down computation and reducing predictive accuracy. By effectively identifying the most essential features, this enhanced algorithm streamlines data preprocessing. This enables data-driven systems to run more efficiently, use less computing power, and deliver more reliable classification outcomes when handling complex, multi-variable data.
This method offers a preprocessing tool for data scientists and software engineers training classification models on high-dimensional data. The abstract shows validation solely on eighteen benchmark datasets from an academic repository. As a result, the technology represents early-stage algorithmic research, requiring practical software library integration and validation on domain-specific industrial datasets before it can be applied in commercial applications.
AI-generated from the published abstract. Always read the original work before citing.
Abstract Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, meta-heuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called Sparrow Search Algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA); and ii) a novel Local Search Algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized. For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, including k -Nearest Neighbor ( k -NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was compared with binary variants of 12 different well-known meta-heuristic algorithms, including the original SSA (BSSA), Artificial Bee Colony (BABC), Particle Swarm Optimization (BPSO), Bat Algorithm (BBA), Grey Wolf Optimization (BGWO), Whale Optimization Algorithm (BWOA), Grasshopper Optimization Algorithm (BGOA) SailFish Optimizer (BSFO), Harris Hawks Optimization (BHHO), Bird Swarm Algorithm (BBSA), Atom Search Optimization (BASO), and Henry Gas Solubility Optimization (BHGSO). Based on a Wilcoxon’s non-parametric statistical test ( $$\alpha =0.05$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>α</mml:mi><mml:mo>=</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math> ), the superiority of iBSSA with the three classifiers was very evident against counterparts across the vast majority of the selected datasets, achieving a feature size reduction of up to 92% along with up to 100% classification accuracy on some of those datasets.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s00521-022-07203-7
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
