A classification method for high-dimensional imbalanced multi-classification data

High-dimensional imbalanced multi-classification problems (HDIMCPs) occur frequently in engineering applications such as medical detection, item classification, and email classification. However, there is a paucity of research in the academic community on this topic. This paper proposes an evolutionary algorithm-based classification method for HDIMCPs, named HIMALO (high-dimensional imbalanced multi-classification method based on ant lion optimizer). HIMALO proposes a new individual initialization strategy that replaces the random initialization of the ant lion optimizer with Fuch chaos. Then, it encodes individuals using concatenated sample features and base classifier weights, optimizes these features and weights concurrently during the iteration process. Additionally, a multi-classification strategy, union one versus many, that combines one versus all and one-against-higher-order is proposed. Numerous experiments are conducted to prove the superior classification performance and stability of HIMALO when compared with other algorithms. This paper proposes an evolutionary algorithm-based classification method for high-dimensional imbalanced multi-classification problems, named HIMALO (high-dimensional imbalanced multi-classification method based on ant lion optimizer). HIMALO proposes a new individual initialization strategy that replaces the random initialization of the ant lion optimizer with Fuch chaos. Then, it encodes individuals using concatenated sample features and base classifier weights, optimizes these features and weights concurrently during the iteration process. Additionally, a multi-classification strategy, union one versus many, that combines one versus all and one-against-higher-order is proposed.image