UM
Kestrel-Based Search Algorithm (KSA) for parameter tuning unto Long Short Term Memory (LSTM) Network for feature selection in classification of high-dimensional bioinformatics datasets
Azbehadji I.E.3; Millham R.3; Fong S.J.1; Yang H.2
2018-10-26
Source PublicationProceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018
Pages15-20
AbstractAlthough deep learning methods have been applied to the selection of features in the classification problem, current methods of learning parameters to be used in the classification approach can vary in terms of accuracy at each time interval, resulting in potentially inaccurate classification. To address this challenge, this study proposes an approach to learning these parameters by using two different aspects of Kestrel bird behavior to adjust the learning rate until the optimal value of the parameter is found: random encircling from a hovering position and learning through imitation from the well-adapted behaviour of other Kestrels. Additionally, deep learning method (that is, recurrent neural network with long short term memory network) was applied to select features and the accuracy of classification. A benchmark dataset (with continuous data attributes) was chosen to test the proposed search algorithm. The results showed that KSA is comparable to BAT, ACO and PSO as the test statistics (that is, Wilcoxon signed rank test) show no statistically significant differences between the mean of classification accuracy at level of significance of 0.05. However, KSA, when compared with WSA-MP, shows a statistically significant difference between the mean of classification accuracy.
KeywordDeep learning Kestrel-based search algorithm Long short term memory network Random encircling
DOI10.15439/2018F52
URLView the original
Language英語
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.University of Leicester
3.Durban University of Technology
Recommended Citation
GB/T 7714
Azbehadji I.E.,Millham R.,Fong S.J.,et al. Kestrel-Based Search Algorithm (KSA) for parameter tuning unto Long Short Term Memory (LSTM) Network for feature selection in classification of high-dimensional bioinformatics datasets[C],2018:15-20.
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