Feature Selection Using Evolutionary Techniques

Mandana Gholami,Malek Mouhoub,Samira Sadaoui

IEEE International Conference on Systems, Man and Cybernetics (SMC)（2023）CCF C

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摘要

Data clustering has many applications in machine learning, data mining and image processing. K-means is the most popular clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has limitations, such as large feature spaces, which may affect its effectiveness. To improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) evolutionary technique to select the most relevant features of datasets. We conducted several experiments to compare our approach with other methods, such as PCA and Particle Swarm Optimization (PSO). The results demonstrate the effectiveness of BBO for feature selection.

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关键词

Development Of Techniques,Image Processing,Data Mining,Feature Space,Clustering Algorithm,Data Clustering,Particle Swarm,Particle Swarm Optimization,Dynamic Changes,Centroid,Evolutionary Algorithms,Fitness Function,Clustering Results,Two-dimensional Space,K-means Algorithm,Feature Selection Methods,Unlabeled Data,Feature Weights,Curse Of Dimensionality,Feature Selection Algorithm,Irrelevant Features,Davies-Bouldin Index,Feature Selection Approach,Irrelevant Ones,Distance Clustering,Clustering Accuracy

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