caa a22 4500 605470464 CHVBK 20210128100328.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00500-014-1339-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1339-0 Instance selection by genetic-based biological algorithm [Elektronische Daten] [Zong-Yao Chen, Chih-Fong Tsai, William Eberle, Wei-Chao Lin, Shih-Wen Ke] Instance selection is an important research problem of data pre-processing in the data mining field. The aim of instance selection is to reduce the data size by filtering out noisy data, which may degrade the mining performance, from a given dataset. Genetic algorithms have presented an effective instance selection approach to improve the performance of data mining algorithms. However, current approaches only pursue the simplest evolutionary process based on the most reasonable and simplest rules. In this paper, we introduce a novel instance selection algorithm, namely a genetic-based biological algorithm (GBA). GBA fits a "biological evolution” into the evolutionary process, where the most streamlined process also complies with the reasonable rules. In other words, after long-term evolution, organisms find the most efficient way to allocate resources and evolve. Consequently, we can closely simulate the natural evolution of an algorithm, such that the algorithm will be both efficient and effective. Our experiments are based on comparing GBA with five state-of-the-art algorithms over 50 different domain datasets from the UCI Machine Learning Repository. The experimental results demonstrate that GBA outperforms these baselines, providing the lowest classification error rate and the least storage requirement. Moreover, GBA is very computational efficient, which only requires slightly larger computational cost than GA. Springer-Verlag Berlin Heidelberg, 2014 Instance selection nationallicence Data reduction nationallicence Data mining nationallicence Machine learning nationallicence Genetic algorithms nationallicence Biological evolution nationallicence Chen Zong-Yao Department of Information Management, National Central University, Jhongli, Taiwan aut Tsai Chih-Fong Department of Information Management, National Central University, Jhongli, Taiwan aut Eberle William Department of Computer Science, Tennessee Technological University, Cookeville, USA aut Lin Wei-Chao Department of Computer Science and Information Engineering, Hwa Hsia Institute of Technology, New Taipei, Taiwan aut Ke Shih-Wen Department of Information and Computer Engineering, Chung Yuan Christian University, Jhongli, Taiwan aut Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1269-1282 1432-7643 19:5<1269 2015 19 500 https://doi.org/10.1007/s00500-014-1339-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1339-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Zong-Yao Department of Information Management, National Central University, Jhongli, Taiwan aut NATIONALLICENCE 700 1- Tsai Chih-Fong Department of Information Management, National Central University, Jhongli, Taiwan aut NATIONALLICENCE 700 1- Eberle William Department of Computer Science, Tennessee Technological University, Cookeville, USA aut NATIONALLICENCE 700 1- Lin Wei-Chao Department of Computer Science and Information Engineering, Hwa Hsia Institute of Technology, New Taipei, Taiwan aut NATIONALLICENCE 700 1- Ke Shih-Wen Department of Information and Computer Engineering, Chung Yuan Christian University, Jhongli, Taiwan aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1269-1282 1432-7643 19:5<1269 2015 19 500