Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers

Summrina Kanwal*, Amir Hussain, Kaizhu Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number113834
JournalExpert Systems with Applications
Volume165
DOIs
Publication statusPublished - 1 Mar 2021

Keywords

  • Artificial Immune Network (AIN)
  • Echo State Network (ESN)
  • Extreme Learning Machine (ELM)
  • Hyper-Parameters Optimization
  • Support Vector Machine (SVM)

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