Optimizing Cuckoo Feature Selection Algorithm with the New Initialization Strategy and Fitness Function

Abstract

In machine learning and data mining tasks, feature selection is an important process of data preprocessing. Recent studies have shown that Binary Cuckoo Search Algorithm for Feature Selection (BCS [1]) has the better ability to classification and dimension reduction. However, by analyzing BCS algorithm, we notice that the randomness of initialization and the defects of fitness function severely weaken the classification performance and dimension reduction. Therefore, we propose a new feature selection algorithm FS\(\_\)CSO, which adopts the chaotic properties of the Chebyshev as a new initialization strategy to get the better original populations (solutions), and combines the information gain and the L1-norm as a new fitness function to accelerate the convergence of the algorithm. We validate FS\(\_\)CSO with various experimental data on small, medium and large datasets on the UCI dataset. In the experiment, FS\(\_\)CSO uses the KNN, J48 and SVM classifiers to guide the learning process. The experimental results show that FS\(\_\)CSO has a significant improvement in classification performance and dimension reduction. Comparing the FS\(\_\)CSO algorithm with the more efficient feature selection algorithms proposed in recent years, FS\(\_\)CSO is highly competitive in terms of accuracy and dimension reduction.