A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data

Abstract

For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…They statistically quantified the results using the bootstrap approach with very high confidence intervals, which showed promising results compared with the original FAM and other procedures. Yun-Tao et al [ 275 ] proposed a new two-phase evolutionary feature selection technique called clustering-guided integer brainstorm optimization algorithm (IBSO-C). The study introduced a new strategy and an integer update scheme for improving the search performance of individuals in BSO.…”