No abstract

Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.

Prevalent adoption of machine learning has magnified its requirements in high dimensional microarray data classification. Due to explosive increase of data dimensionality, the existence of features redundancy and ambiguity directly leads to classification inaccuracy. Filter feature selection algorithms are capable to boost classification accuracy and diminish computational complexity by extracting relevant information through supervised learning. However, the independent filter algorithm is incompetent to consider the features interaction which resulting an imbalance selection of significant features and consequently degrading the classifier performance. This paper presents an assemblage of multi filters algorithm which assembles four filters algorithm outputs with frequency of occurrence rate evaluation to improve classification performance by attaining an optimal number of significant features. Experimental analysis was performed on a standard Breast Cancer dataset consists of 286 instances and Support Vector Machine (SVM) classifier. The experimental results proved that the ensemble based multi filters algorithm with occurrence rate evaluation successfully depletes from 9 original dataset features to 5 optimal significant features. The finding indicates that this technique competently signifies SVM classification performance in terms of accuracy with optimum significant features for high dimensional microarray data compared to independent filter algorithm.