计算机科学
聚类分析
特征选择
数据挖掘
启发式
机器学习
任务(项目管理)
人工智能
维数之咒
选择(遗传算法)
特征(语言学)
算法
语言学
哲学
管理
经济
作者
Rajesh Ranjan,Jitender Kumar Chhabra
标识
DOI:10.1016/j.asoc.2023.110305
摘要
Today's real-world data is frequently significant in size, with many redundant, missing, and noise-based features and data instances must be addressed before applying various data-mining-based algorithms for further knowledge discovery. Excessive dimensionality may be mitigated by carefully excluding unnecessary characteristics and selecting a reasonable subset of features. When presented as an optimization issue, choosing the best clusters using the most suitable subset of attributes is a challenge that may be handled using practical meta-heuristic approaches. Besides this, the automatic finding of the appropriate cluster number is another challenging task for the real-world dataset in the unsupervised machine-learning study. The present work proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism for a better exploration phase of the crow and further making it suitable for multi-objective optimization problems. MO-CSACFS addresses both issues using the three objective functions to find appropriate cluster numbers and features. MO-CSACFS is implemented over several real-life and synthetic datasets with varying instances, features, and cluster numbers to assess the algorithm's performance; apart from that, the present work is also applied over several gene-expression datasets. MO-CSACFS is compared with two similar recently proposed multi-objective optimization processes used over an automatic, unsupervised machine learning task. The results show that the MO-CSACFS has produced a compact and robust cluster comparable to other similar works from the literature.
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