Evolutionary Cost-sensitive Extreme Learning Machine

TL;DR

This paper introduces ECSELM, an evolutionary approach to cost-sensitive extreme learning machines that adaptively optimizes the cost matrix, significantly improving classification performance in cost-sensitive tasks.

Contribution

It is the first to apply evolutionary methods to optimize cost-sensitive ELMs and addresses the challenge of defining the cost matrix in such tasks.

Findings

Achieved 5-10% performance improvements.

Effectively optimizes cost matrix adaptively.

Demonstrated success across various cost-sensitive tasks.

Abstract

Conventional extreme learning machines solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition based access control system, where misclassifying a stranger as a family member may result in more serious disaster than misclassifying a family member as a stranger. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another, in many realistic cases the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive extreme learning machine (ECSELM), with the following merits: 1) to our best knowledge, it is the first proposal of ELM in evolutionary cost-sensitive classification scenario; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Experiments in a variety of cost-sensitive tasks well demonstrate the effectiveness of the proposed approaches, with about 5%~10% improvements.