A Hybrid Algorithm for Feature Selection and Classification

B. R. Sathish,
Radha Senthilkumar,


With a recent spread of intelligent information systems, massive data collections with a lot of repeated and unintentional, unwanted interference oriented data are gathered and a huge feature set are being operated. Higher dimensional inputs, on the other hand, contain more correlated variables, which might have a negative impact on model performance. In our model a Hybrid method of selecting feature was developed by combining Binary Gravitational Search Particle Swarm Optimization (HBGSPSO) method with an Enhanced Convolution Neural Network Bidirectional Long Short Term Memory (ECNN-BiLSTM). In our proposed system, the Bidirectional Long Short Term Memory (BiLSTM) is introduced which extracts the hidden dynamic data and utilizes the memory cells to think of long-term historical data after the convolution process. In this paper, thirteen well-defined datasets are used from the machine learning database of UC Irvine to evaluate the efficiency of the proposed system. The experiments are conducted using K Nearest Neighbor (KNN) and Decision Tree (DT) which are used as classifiers to evaluate the outcome of selected features. The outcomes are contrasted and compared with the bio-enlivened calculations like Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Optimization protocol using Particle Swarm Optimization (PSO).


Feature selection, Genetic algorithm, Convolution neural network, Particle swarm optimization, Grey wolf optimizer

Citation Format:
B. R. Sathish, Radha Senthilkumar, "A Hybrid Algorithm for Feature Selection and Classification," Journal of Internet Technology, vol. 24, no. 3 , pp. 593-602, May. 2023.

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