{"title":"基于概率驱动和神经网络驱动方法的学习分类器系统改进","authors":"Ladislav Clementis","doi":"10.1109/ECBS-EERC.2013.26","DOIUrl":null,"url":null,"abstract":"Rule-based systems like Learning Classifier System are widely used in areas where data mining, data classification and pattern recognition tasks are essential. It is often difficult to address the knowledge base of these complex classifier systems, which is usually a set of classifiers. Therefore we use evolutionary processes like genetic algorithms to develop their knowledge base. We provide modified Learning Classifier System enriched by probability model to help build an appropriate knowledge base more effectively. We included a neural network into the action selection process and therefore action can be determined accordingly with a probability model. We provide simulation results which demonstrate efficiency of learning processes to compare these approaches.","PeriodicalId":314029,"journal":{"name":"2013 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Learning Classifier System Improvement Based on Probability Driven and Neural Network Driven Approaches\",\"authors\":\"Ladislav Clementis\",\"doi\":\"10.1109/ECBS-EERC.2013.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rule-based systems like Learning Classifier System are widely used in areas where data mining, data classification and pattern recognition tasks are essential. It is often difficult to address the knowledge base of these complex classifier systems, which is usually a set of classifiers. Therefore we use evolutionary processes like genetic algorithms to develop their knowledge base. We provide modified Learning Classifier System enriched by probability model to help build an appropriate knowledge base more effectively. We included a neural network into the action selection process and therefore action can be determined accordingly with a probability model. We provide simulation results which demonstrate efficiency of learning processes to compare these approaches.\",\"PeriodicalId\":314029,\"journal\":{\"name\":\"2013 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECBS-EERC.2013.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECBS-EERC.2013.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning Classifier System Improvement Based on Probability Driven and Neural Network Driven Approaches
Rule-based systems like Learning Classifier System are widely used in areas where data mining, data classification and pattern recognition tasks are essential. It is often difficult to address the knowledge base of these complex classifier systems, which is usually a set of classifiers. Therefore we use evolutionary processes like genetic algorithms to develop their knowledge base. We provide modified Learning Classifier System enriched by probability model to help build an appropriate knowledge base more effectively. We included a neural network into the action selection process and therefore action can be determined accordingly with a probability model. We provide simulation results which demonstrate efficiency of learning processes to compare these approaches.