Universiti Teknologi Malaysia Institutional Repository

Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction

Tengku Ab. Hamid, Tengku Mazlin and Sallehuddin, Roselina and Mohd. Yunos, Zuriahati and Ali, Aida (2023) Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction. Neural Computing and Applications, 35 (14). pp. 10473-10496. ISSN 0941-0643

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1007/s00521-023-08244-2

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.

Item Type:Article
Uncontrolled Keywords:feature selection, filters algorithm, particle swarm optimization, support vector machine
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:106030
Deposited By: Yanti Mohd Shah
Deposited On:29 May 2024 06:39
Last Modified:29 May 2024 06:39

Repository Staff Only: item control page