A New Data Mining Scheme Using Artificial Neural Networks

被引:13
作者
Kamruzzaman, S. M. [1 ]
Sarkar, A. M. Jehad [2 ]
机构
[1] Hankuk Univ Foreign Studies, Dept Elect Engn, Yongin 449791, Kyonggi Do, South Korea
[2] Hankuk Univ Foreign Studies, Dept Digital Informat Engn, Yongin 449791, Kyonggi Do, South Korea
关键词
data mining; neural networks; symbolic rules; weight freezing; constructive algorithm; pruning; clustering; rule extraction; RULE EXTRACTION; ALGORITHMS;
D O I
10.3390/s110504622
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.
引用
收藏
页码:4622 / 4647
页数:26
相关论文
共 55 条
[31]   Potential use of Artificial Neural Network in Data Mining [J].
Nirkhi, Smita .
2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 2, 2010, :339-343
[32]  
ODAJIMA K, 2008, NEURAL NETW, V21
[33]   Constructive neural-network learning algorithms for pattern classification [J].
Parekh, R ;
Yang, JH ;
Honavar, V .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2000, 11 (02) :436-451
[34]   RULES - A SIMPLE RULE EXTRACTION SYSTEM [J].
PHAM, DT ;
AKSOY, MS .
EXPERT SYSTEMS WITH APPLICATIONS, 1995, 8 (01) :59-65
[35]   AN ALGORITHM FOR AUTOMATIC RULE INDUCTION [J].
PHAM, DT ;
AKSOY, MS .
ARTIFICIAL INTELLIGENCE IN ENGINEERING, 1993, 8 (04) :277-282
[36]  
PRECHELT L, 1994, 2194 U KARLSR FAK IN
[37]   PRUNING ALGORITHMS - A SURVEY [J].
REED, R .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1993, 4 (05) :740-747
[38]  
SAITO K, 1988, P IEEE INT C NEUR NE, P1255
[39]   Extracting rules from neural networks by pruning and hidden-unit splitting [J].
Setiono, R .
NEURAL COMPUTATION, 1997, 9 (01) :205-225
[40]   Symbolic representation of neural networks [J].
Setiono, R ;
Liu, H .
COMPUTER, 1996, 29 (03) :71-&