Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Abstract

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.

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

“…The heuristic method is based on intuition which employs a practical method that is not guaranteed to be optimal or perfect approach but it is nevertheless sufficient to yield an approximation well [15]. The evaluation of dependency between the target and predictor features, and the evaluation of independence among predictor features are key concepts in running the heuristic method [16]. There are 3 statistical tests usually used for the dependency or independency test namely the Chi-square test for evaluating dependency among 2 categorical features [17], Pearson's correlation test for evaluating dependency among two numerical features [18], and one-way Analysis of Variance (ANOVA) for evaluating dependency between the categorical and numerical features [19].…”

An improved manta ray foraging optimization algorithm

“…This is because MH algorithms generate a feasible solution space with a stochastic algorithm, search the space for solutions in each iteration, evaluate individual fitness through the fitness function, and perform updates to produce the optimal solution 3 . The MH algorithms have shown advantages in many fields, including global optimization 4 , 5 , feature selection 6 , 7 , sentiment classification 8 – 10 , and case forecasting 11 . Considering the no free lunch (NFL) theorem, no algorithm can perform well on every optimization problem 12 , it is significant to study the MH algorithms.…”