Bacterial colony algorithm with adaptive attribute learning strategy for feature selection in classification of customers for personalized recommendation

This paper investigates a new bacterial colony-based feature selection algorithm to improve the classifi-cation accuracy of customers for personalized products recommendation. An attribute learning strategy is developed in this study to update the feature related population. Specifically, the features can be weighted according to their historic contributions to both the individual-and group-based subsets. Additionally, the frequency of appearance is also recorded for the feature candidates to improve the diversity of feature distribution and avoid the over-fitting. Based on the weight-based feature indexes and frequency of appearance records, the performance of feature subsets are enhanced by replacing the features being repeatedly appeared in a same vector. To explore the feasibility of the proposed method for the missing feature problems, the objective of the optimization is to minimize the classifica-tion error using the acceptable number of features. K-Nearest Neighbor is employed as the learning tech-nique to cooperate with the proposed feature selection method. The effectiveness of the proposed feature selection method is demonstrated by performing test on the datasets from UCI machine learning repos-itory and real-world data from Amazon customer reviews of products. Compared with other seven fea-ture selections methods, the proposed feature selection algorithm outperforms the other algorithms by achieving higher classification accuracy rate using smaller features. (c) 2020 Elsevier B.V. All rights reserved.