## Kestrel-based Search Algorithm (KSA) and Long Short Term Memory (LSTM) Network for feature selection in classification of high-dimensional bioinformatics datasets

### Israel Edem Agbehadji, Richard Millham, Simon James Fong, Hongji Yang

DOI: http://dx.doi.org/10.15439/2018F52

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 15–20 (2018)

Abstract. Although 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 accuracy at each time interval, resulting in potentially inaccurate classification of features. 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. The proposed bio-inspired approach is integrated with deep learning method (that is, recurrent neural network with long short term memory network). 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

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