Sequential Transfer with Multi-Objective Genetic Algorithm for Feature Selection of Small, High-Dimensional Datasets

Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined
$n$
number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the
$n$
features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.