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

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 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.Index Terms-kestrel-based search algorithm, deep learning, random encircling, long short term memory network.

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

“…NIC algorithms are mainly categorized into evolutionary algorithms and swarm intelligence-based algorithms. Evolutionary algorithms are based on the evolutionary behavior of natural systems, e.g., genetic algorithm (GA), whereas swarm intelligence mimics the collective behavior of natural swarms, e.g., the ant colony algorithm (ACO) [72], and the wolf [73], ANT [74], dung beetle [75], particle swarm optimization (PSO) [76,77] and BAT [78] algorithms, plus many more. Table 3 shows the nature-inspired algorithms and application domains.…”

“…Kestrel-based Search Algorithm (KSA) KSA was applied as a parameter tuning algorithm to improve on the accuracy of feature selection in high-dimensional bioinformatics datasets [76].…”

Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

“…A GA is an evolutionary approach that is based on the survival of the fittest. This survival depends on the mechanism of ''natural selection'' (Darwin, 1868 as cited by Agbehadji et al 29 ) where species considered as weak and cannot adapt to the conditions of the habitat are eliminated while species considered as strong and can adapt to the habitat survive. Thus, natural selection is based on the notion that strong species have a greater chance to pass their genes to future generations, while weaker species are eliminated by natural selection.…”

“…44 Although the algorithm has been applied to different problem domains such as missing value estimation, 44 association rule mining, 46 and feature selection in classification. 29,47 In this study, we applied the KSA to clustering in the case of heterogeneous energy requirements. The aim of clustering using the Kestrel is to optimize energy consumption by balancing nodes on the edge computing network.…”

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“…Especially the independent variable is the predicted value while the dependent variable is the actual value. In this paper [6], the LSTM model was used to select features and improve classification accuracy. [7] To forecast the confidence interval, the pinball loss function was integrated with the LSTM network.…”