Ensemble Based Filter Feature Selection with Harmonize Particle Swarm Optimization and Support Vector Machine for Optimal Cancer Classification

• Main Authors: Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Machine Learning with Applications
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666827021000281

Explosive increase of dataset features may intensify the complexity of medical data analysis in deciding necessary treatment for the patient. In most cases, the accuracy of diagnosis system is vitally impacted by the data dimensionality and classifier parameters. Since these two processes are dependent, conducting them independently could deteriorate the accuracy performance. Filter algorithm is used to eliminate irrelevant features based on ranking. However, independent filter still incapable to consider features dependency and resulting in imbalance selection of significant features which consequently degrade the classification performance. In order to mitigate this problem, ensemble of multi filters algorithm such as Information Gain (IG), Gain Ratio (GR), Chi-squared (CS) and Relief-F (RF) are utilized as it can considers the intercorrelation between features. The proper kernel parameters settings may also influence the classification performance. Hence, a harmonize classification technique using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is employed to optimize the searching of optimal significant features and kernel parameters synchronously without degrading the accuracy. Therefore, an ensemble filter feature selection with harmonize classification of PSO and SVM (Ensemble-PSO-SVM) are proposed in this research. The effectiveness of the proposed method is examined on standard Breast Cancer and Lymphography datasets. Experimental results showed that the proposed method successfully signify the classifier accuracy performance with optimal significant features compared to other existing methods such as PSO-SVM and classical SVM. Hence, the proposed method can be used as an alternative method for determining the optimal solution in handling high dimensional data.