A hybrid grasshopper and new cat swarm optimization algorithm for feature selection and optimization of multi-layer perc
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METHODOLOGIES AND APPLICATION
A hybrid grasshopper and new cat swarm optimization algorithm for feature selection and optimization of multi-layer perceptron Priti Bansal1 • Sachin Kumar1 • Sagar Pasrija1 • Sachin Singh1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The classification accuracy of a multi-layer perceptron (MLP) depends on the selection of relevant features from the data set, its architecture, connection weights and the transfer functions. Generating an optimal value of all these parameters together is a complex task. Metaheuristic algorithms are popular choice among researchers to solve complex optimization problems. This paper presents a hybrid metaheuristic algorithm simple matching-grasshopper new cat swarm optimization algorithm (SM-GNCSOA) that optimizes all the four components simultaneously. SM-GNCSOA uses grasshopper optimization algorithm, a new variant of binary grasshopper optimization algorithm called simple matching-binary grasshopper optimization algorithm and a new variant of cat swarm optimization algorithm called new cat swarm optimization algorithm to generate an optimal MLP. Features play a vital role in determining the classification accuracy of a classifier. Here, we propose a new feature penalty function and use it in SM-GNCSOA to prevent underfitting or overfitting due to the selected number of features. To evaluate the performance of SM-GNCSOA, different variants of SM-GNCSOA are proposed and their classification accuracies are compared with SM-GNCSOA on ten classification data sets. The results show that SM-GNCSOA gives better results on most of the data sets due to its capability to balance exploration and exploitation and to avoid local minima. Keywords Simple matching distance Binary grasshopper optimization algorithm New cat swarm optimization algorithm Feature selection Multi-layer perceptron
1 Introduction Artificial neural networks (ANNs) are computational models that mimic human brain and are widely used to model complex nonlinear problems. Due to its learning capabilities, ANNs are widely used in the field of data classification, forecasting, face identification and pattern
Communicated by V. Loia. & Priti Bansal [email protected] Sachin Kumar [email protected] Sagar Pasrija [email protected] Sachin Singh [email protected] 1
Department of Information Technology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
recognition (Chen and Zhang 2009; Rezaeianzadeh et al. 2014; Va´zquez et al. 2010; Schmidhuber 2015). Among ANN topologies, feedforward multi-layer perceptrons (MLPs) are generally preferred to solve classification problems. An important characteristic of MLP is its ability to learn from data. In today’s scenario, tremendous amount of high dimensionality data is being generated every day which poses a challenge for data analysts. In addition to this, data sets may contain redundant and irrelevant features which do not contribute much to the
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