The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Algoritmos de aprendizado de máquina estão se tornando cada vez mais populares na era atual. O pré-processamento de dados, especialmente a seleção de recursos, é útil para melhorar o desempenho desses algoritmos. Um novo e poderoso algoritmo de seleção de recursos é proposto. Ele combina as vantagens da otimização de colônias de formigas e da otimização de tempestade cerebral que simula o comportamento dos seres humanos. Seis conjuntos de dados clássicos e cinco algoritmos de última geração são usados para fazer uma comparação com nosso algoritmo em problemas de classificação binária. Os resultados de precisão, taxa percentual, taxa de recall e medidas F1 mostram que o algoritmo desenvolvido é mais excelente. Além disso, não é mais complexo do que as abordagens comparadas.
Haomo LIANG
Army Engineering University of PLA
Zhixue WANG
Army Engineering University of PLA
Yi LIU
National Innovation Institute of Defense Technology
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Haomo LIANG, Zhixue WANG, Yi LIU, "A New Hybrid Ant Colony Optimization Based on Brain Storm Optimization for Feature Selection" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1396-1399, July 2019, doi: 10.1587/transinf.2019EDL8001.
Abstract: Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8001/_p
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@ARTICLE{e102-d_7_1396,
author={Haomo LIANG, Zhixue WANG, Yi LIU, },
journal={IEICE TRANSACTIONS on Information},
title={A New Hybrid Ant Colony Optimization Based on Brain Storm Optimization for Feature Selection},
year={2019},
volume={E102-D},
number={7},
pages={1396-1399},
abstract={Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.},
keywords={},
doi={10.1587/transinf.2019EDL8001},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - A New Hybrid Ant Colony Optimization Based on Brain Storm Optimization for Feature Selection
T2 - IEICE TRANSACTIONS on Information
SP - 1396
EP - 1399
AU - Haomo LIANG
AU - Zhixue WANG
AU - Yi LIU
PY - 2019
DO - 10.1587/transinf.2019EDL8001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.
ER -