基于混沌二元蚁狮优化的COVID-19医疗数据集特征选择方法

M. Zivkovic, N. Bačanin, Andjela Rakic, Jelena Arandjelovic, Stefan Stanojlovic, K. Venkatachalam
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引用次数: 3

摘要

提出了蚂蚁狮子优化器(ALO)的二进制版本,并在包装模式下使用它来选择最佳的特征子集进行分类。ALO是最近开发的一种仿生优化方法,模仿蚂蚁狮子的狩猎行为。此外,ALO利用一个独特的算子来平衡探索和利用,自适应地探索解决方案的空间,以获得最佳解决方案。大量嘈杂、不相关和误导性特征的困难,以及处理现实世界主题中不正确和不一致数据的能力,为特征选择成为最重要的需求之一提供了基本原理。一个困难的机器学习问题是从描述数据集的大量特征中选择重要特征的子集。选择信息量最大的标记并对数据进行高精度分类可能是一个困难的过程,特别是在数据很复杂的情况下。特征选择任务通常被表示为生物目标优化挑战,其目标是在减少使用特征数量的同时提高预测模型的性能(数据训练拟合质量)。采用了各种评价标准来确定所建议的方法是否成功。研究结果表明,混沌二值算法可以有效地探索特征空间以获得最优特征集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study
Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently.
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