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Abstract：
Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.
摘要：
特征选择是机器学习和数据挖掘的关键组成部分，它解决了诸如不相关之类的挑战，噪音,大规模数据的冗余等。,这往往会导致维度的诅咒。本研究采用K最近邻包装器，使用六种自然启发算法实现特征选择，源自人类行为和哺乳动物启发的技术。在六个现实世界的数据集上评估，这项研究旨在比较这些算法在准确性方面的性能，特征计数，健身,收敛性和计算成本。这些发现强调了人类学习优化的有效性，跨多个性能指标的差而丰富的优化和灰狼优化器算法。例如,为了卑鄙的健身，人类学习优化优于其他人，其次是可怜和丰富的优化和和谐搜索。这项研究表明了人类启发算法的潜力，特别是差的和丰富的优化，在不影响分类精度的情况下进行鲁棒特征选择。
