Analysis of Feature Selection Algorithms on Classification: A Survey
作者： S. Vanaja , K. Ramesh Kumar
DOI: 10.5120/16888-6910
关键词: Artificial neural network 、 Genetic algorithm 、 Association rule learning 、 Linear classifier 、 Knowledge representation and reasoning 、 One-class classification 、 Data mining 、 Feature selection 、 Naive Bayes classifier 、 Machine learning 、 Rule-based system 、 Decision tree 、 Rough set 、 Computer science 、 Artificial intelligence 、 Backpropagation 、 Support vector machine 、 Fuzzy set 、 Knowledge extraction
摘要: 1. INTRODUCTIONmining is a process of knowledge discovery. The KDD an automated discovery from the original data. consists many steps like data cleaning, integration, selection, transformation, mining, pattern evaluation and representation. Among selection very much important to select relevant feature remove irrelevant attributes. Classification one datamining techniques used discover unknown class. different classification methods in mining are Bayesian (Statistical classifier), Decision tree induction, Rule based (IF THEN Rule), using Back propagation (Neural network algorithm), Support vector machine, Association Rule, k-nearest neighbor classifiers, casebased reasoning Rough set approach, Genetic algorithm, Fuzzy approach.
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