Evolutionary feature and instance selection for traffic sign recognition

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題名:	Evolutionary feature and instance selection for traffic sign recognition
作者:	柯士文;Chen, Zong-Yao;Lin, Wei-Chao;Ke, Shih-Wen;Tsai, Chih-Fong
貢獻者:	管理學院資訊管理學系
關鍵詞:	Algorithms;Computer simulation;Data mining;Evolutionary;Feature recognition;Feature selection;Genetic algorithm;Genetic algorithms;Instance selection;Object recognition;Preprocessing;Traffic sign recognition;Traffic signs
日期:	2015-01-21
上傳時間:	2026-04-23 13:34:49 (UTC+8)
出版者:	Elsevier;Elsevier B.V
摘要:	摘要： •The effect of performing feature and instance selection on traffic sign recognition is examined.•A genetic-based biological algorithm (GBA) is proposed for effective traffic sign recognition.•GBA produces better feature and instance selection results than GA.•Moreover, GBA outperforms GA in terms of reduction rate and computational cost. The problem of traffic sign recognition is generally approached by first constructing a classifier, which is trained by some relevant image features extracted from traffic signs, to recognize new unknown traffic signs. Feature selection and instance selection are two important data preprocessing steps in data mining, with the former aimed at removing some irrelevant and/or redundant features from a given dataset and the latter at discarding the faulty data. However, there has thus far been no study examining the impact of performing feature and instance selection on traffic sign recognition performance. Given that genetic algorithms (GA) have been widely used for these types of data preprocessing tasks in related studies, we introduce a novel genetic-based biological algorithm (GBA). GBA fits “biological evolution” into the evolutionary process, where the most streamlined process also complies with reasonable rules. In other words, after long-term evolution, organisms find the most efficient way to allocate resources and evolve. Similarly, we closely simulate the natural evolution of an algorithm, to find an option it will be both efficient and effective. Experiments are carried out comparing the performance of the GBA and a GA based on the German Traffic Sign Recognition Benchmark. The results show that the GBA outperforms the GA in terms of the reduction rate, classification accuracy, and computational cost. 出版者： Elsevier B.V 出版日期： 2015-12-01 出處： Computers in industry, 2015-12, Vol.74, p.201-211 版權： 2015 Elsevier B.V. 識別號： ISSN: 0166-3615 識別號： EISSN: 1872-6194 識別號： DOI: 10.1016/j.compind.2015.08.007
顯示於類別:	[資訊管理學系] 期刊論文

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