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doi:10.29126/23951303/ijet-v4i2p33

Cited by 1 publication

(3 citation statements)

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“…This means different datasets may require different threshold values [ 26 , 13 ]. However, since there are no specific accurate values, the threshold values that have been proposed by Dongare et al [ 16 ], Urbanowicz et al [ 58 ], Fahrudin et al [ 17 ], and Dai and Xu [ 14 ] are employed. In order to optimize the suggested threshold values, the classification accuracy at different thresholds is calculated.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The classification accuracy performance also increases from 75.05 to 91.20% with a reduced set of features. Dongare et al [ 16 ] considered CFS, SU, RF, IG, and CS as feature selection methods to improve the classification performance for Cardiotocography class prediction using Naïve Bayes (NB) classifier. The results of classification performance successfully selected 12 out of 23 original features as the most significant features with 86.78% of accuracy.…”

Section: Literature Studymentioning

confidence: 99%

“…These filters are highly preferred as they constantly improve classification accuracy by using a simple statistical ranking score evaluation. The improvement in each classifiers accuracy using ensemble RF, IG, GR, and CS in Lee et al [ 29 ], Santos et al [ 49 ], and Dongare et al [ 16 ] indicate that ensemble filters are capable in selecting significant amount of features by considering dependency between features. Furthermore, the experiment conducted by Raj and Mohanasundaram [ 44 ] clearly shows that the accuracy improved significantly to 92.30% compared to the accuracy with full dataset features.…”

Section: Literature Studymentioning

confidence: 99%

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

Hamid

Sallehuddin

Yunos

et al. 2023

Neural Comput & Applic

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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.

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