Search

Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers. [ABSTRACT FROM AUTHOR]

The main challenge of feature selection is overcoming the curse of dimensionality. In this paper, a new Bacterial Colony Optimization method with Multi-Dimensional Population, abbreviated as BCO-MDP, is presented for feature selection for the purpose of classification. To address the combinational problem associated with feature selection, the population with multiple dimensionalities is represented by subsets of different feature sizes. The population is grouped in terms of 'Tribes'. The sizes of the feature subsets within a tribe are equal, while the dimensionalities differ when they belong to different tribes to achieve parallel solutions. The features are identified by their contributions to the most promising solutions in the total population and the classification performances of their tribes. A search is then conducted for the optimal feature subsets with varying dimensionalities. The convergence speed can be enhanced by a variety of exchange strategies within and between tribes. The proposed BCO-MDP method is demonstrated to be superior to the binary algorithms in terms of feature size and efficiency, while having a lower computational complexity in comparison to other population-based algorithms with constant dimensionality. [ABSTRACT FROM AUTHOR]

This paper investigates a new bacterial colony-based feature selection algorithm to improve the classification 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 classification error using the acceptable number of features. K-Nearest Neighbor is employed as the learning technique 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 repository and real-world data from Amazon customer reviews of products. Compared with other seven feature selections methods, the proposed feature selection algorithm outperforms the other algorithms by achieving higher classification accuracy rate using smaller features. [ABSTRACT FROM AUTHOR]