ABSTRACT

Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms.

摘要

从生物学数据中找到相关信息是疾病诊断研究的关键问题，尤其是当涉及大量生物学特征时。故意地，特征选择可以是分类阶段之前的必要的预处理步骤。平衡优化器（EO）是最近建立的一种启发式算法，受启发于在测量平衡状态时动态源模型和宿模型的原理。在这项研究中，提出了一种称为通用学习均衡优化器（GLEO）的EO新变体，作为包装特征选择方法。此方法采用一般的学习策略，以帮助粒子逃避本地区域并提高发现有希望的区域的能力。拟议的GLEO旨在识别大量属性中信息丰富的生物学特征的子集。GLEO算法的性能已在16个生物学数据集上得到验证，其中有9个生物学数据集代表了高维数，且实例数量较少。获得的结果表明，与其他元启发式算法相比，GLEO在适应性值，准确性和特征大小方面均具有出色的性能。