A New Hybrid Feature Selection Method Using T-test and Fitness Function

Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes the well-designed fitness function to calculate the strength of recognition value (SoRV). The two values are used to rank all features according to the final weight (FW) calculated for each feature subset using a function that prioritizes feature subsets with high SoRV values. An FW is assigned to each feature subset, and those with FWs less than a predefined threshold are removed from the feature subset domain. Experiments are implemented on three datasets: Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin, and Surrey Audio-Visual Expressed Emotion. The performance of the F-test and F-score FS methods are compared to those of the proposed method. Tests are also conducted on a system before and after deploying the FS methods. Results demonstrate the comparative efficiency of the proposed method. The complexity of the system is calculated based on the time overhead required before and after FS. Results show that the proposed method can reduce system complexity.