篇名 |
A Hybrid Algorithm for Feature Selection and Classification
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並列篇名 | A Hybrid Algorithm for Feature Selection and Classification |
作者 | B. R. Sathish、Radha Senthilkumar |
英文摘要 | 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).
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起訖頁 | 593-602 |
關鍵詞 | Feature selection、Genetic algorithm、Convolution neural network、Particle swarm optimization、Grey wolf optimizer |
刊名 | 網際網路技術學刊 |
期數 | 202305 (24:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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