Optimal-margin evolutionary classifier

TL;DR

This paper presents a new evolutionary algorithm for discriminative classification that optimizes hyperplanes, demonstrating superior accuracy and robustness against noise and outliers across multiple benchmark datasets.

Contribution

The paper introduces a novel evolutionary classification method that optimizes a total loss function for hyperplane-based classification, outperforming existing algorithms.

Findings

Significantly better classification accuracy than state-of-the-art methods.

Enhanced robustness against noise and outliers.

Reasonable computational time for real-world applications.

Abstract

We introduce a novel approach for discriminative classification using evolutionary algorithms. We first propose an algorithm to optimize the total loss value using a modified 0-1 loss function in a one-dimensional space for classification. We then extend this algorithm for multi-dimensional classification using an evolutionary algorithm. The proposed evolutionary algorithm aims to find a hyperplane which best classifies instances while minimizes the classification risk. We test particle swarm optimization, evolutionary strategy, and covariance matrix adaptation evolutionary strategy for optimization purpose. Finally, we compare our results with well-established and state-of-the-art classification algorithms, for both binary and multi-class classification, on 19 benchmark classification problems, with and without noise and outliers. Results show that the performance of the proposed algorithm is significantly (t-test) better than all other methods in almost all problems tested. We also show that the proposed algorithm is significantly more robust against noise and outliers comparing to other methods. The running time of the algorithm is within a reasonable range for the solution of real-world classification problems.