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Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers.
- Source :
-
Expert Systems with Applications . Mar2021, Vol. 165, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- • Artificial immune network (AIN) for optimizing machine learning algorithms. • Convergence analysis of the proposed optimization algorithm is presented. • Successful experimentation on many benchmark machines learning datasets. • Achieved classification performance boost of 2% – 9%. Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel application of the AIN for optimizing shallow machine learning (ML) classification algorithms. AIN accomplishes this task by searching the best hyper-parameter set for a specific classification algorithm (also termed model selection), which minimizes training error and enhances the generalization capability of the algorithm. We present a convergence analysis of the proposed algorithm and employ it in conjunction with selected, well-known ML classifiers, namely, an extreme learning machine (ELM), a support vector machine (SVM) and an echo state network (ESN). The performance is evaluated in terms of classification accuracy and learning time, using a range of benchmark datasets, and compared against grid search as well as evolutionary strategy (ES)-based optimization techniques. An empirical study with different datasets demonstrates improved classification accuracy of SVM, from 2% to 5%, for ESN from 3% to 6%, whereas in the case of ELM from 3% to 9%. Comparative simulation results demonstrate the potential of AIN as an alternative optimizer for shallow ML algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 165
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 147583965
- Full Text :
- https://doi.org/10.1016/j.eswa.2020.113834