Abstract Neural network technology has already been applied in a variety of domains with remarkable success. However, it has not been well utilized in data mining and knowledge discovery. In this paper, a general neural framework named NEUCRUM is proposed for classification rule mining. This paper also presents a possible implementation of NEUCRUM whose key components are a specific neural classifier named FANNC and a novel rule extraction approach named STARE. FANNC is used to learn from pre-processed data, in which its fast learning speed and strong generalization ability are quite contributive. STARE is proposed in this paper, which is used to extract comprehensible, compact and accurate symbolic rules from trained neural networks so that the knowledge discovered is explicitly available to decision-makers. Applications show that NEUCRUM and its implementation described in this paper work well in many real domains.
|
4514
|
Statistical Learning Theory
– Vapnik
- 1998
|
|
2138
|
UCI Repository of Machine Learning Databases
– Merz, Murphy
- 1996
|
|
509
|
C4.5: Programs for
– Quinlan
- 1993
|
|
431
|
Learning representation by back propagating errors
– Rumelhart, Hinton, et al.
- 1986
|
|
255
|
Adaptive pattern classification and universal recoding: Part I. Parallel development and codings of neural feature detectors
– Grossberg
- 1976
|
|
213
|
Database mining: A performance perspective
– Agrawal, Imielinski, et al.
- 1993
|
|
185
|
Computer Systems that Learn: Classification and Prediction Methods from Statistics
– Weiss, Kulikowski
- 1991
|
|
179
|
A survey and critique of techniques for extracting rules from trained neural networks. Knowledge Based Systems. 8(6): 373–389. nants, such as ‘Exports of goods’, ‘Imports of goods’, ‘Scientific publications’ and ‘Tax revenue’. The results indicate that ac
– Andrews, Diederich, et al.
- 1995
|
|
170
|
Extracting refined rules from knowledge-based neural networks
– Towell, Shavlik
- 1993
|
|
136
|
Chimerge: Discretization for numeric attributes
– KERBER
- 1992
|
|
109
|
Connectionist expert system
– Gallant
- 1988
|
|
107
|
Real-world data is dirty: Data cleansing and the merge/purge problem
– Hernández, Stolfo
- 1998
|
|
85
|
Knowledge discovery and data mining: towards a unifying framework
– Fayyed, Piatetsky-Shapiro, et al.
- 1996
|
|
71
|
Rule learning by searching on adapted nets
– Fu
- 1991
|
|
62
|
sampling and queries to extract rules from trained neural networks
– Craven, Shavlik
- 1994
|
|
59
|
The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks
– Tickle, Andrews, et al.
- 1998
|
|
32
|
Extracting rules from artificial neural networks with distributed representations
– Thrun
- 1995
|
|
28
|
Extracting Rules from Neural Networks by Pruning and Hidden-unit Splitting
– Setiono
- 1997
|
|
28
|
Symbolic interpretation of artificial neural networks
– Taha, Gosh
- 1999
|
|
27
|
Are artificial neural networks black boxes
– Benitez, Castro, et al.
- 1997
|
|
24
|
Using Neural Networks for Data Mining. Future Generation Computer Systems
– Craven, Shavlik
- 1997
|
|
16
|
Neural data mining for credit card fraud detection
– Brause, Langsdorf, et al.
- 1999
|
|
15
|
Effective data mining using neural networks
– Lu, Setiono, et al.
- 1996
|
|
14
|
Extracting rules from trained neural networks
– Tsukimoto
- 2000
|
|
13
|
Selected techniques for data mining in medicine
– Lavrac
- 1999
|
|
10
|
Data strip mining for the virtual design of pharmaceuticals with neural networks
– Kewley, Embrechta, et al.
- 2000
|
|
10
|
FANNC: A Fast Adaptive Neural Network Classifier
– Zhou, Chen, et al.
- 2000
|
|
8
|
Knowledge acquisition of conjunctive rules using multilayered neural networks
– Sestito, Dillon
- 1993
|
|
8
|
Rule extraction from facts and neural networks
– Saito, Nakano
- 1990
|
|
7
|
A Hybrid Approach of Neural Network and Memory-Based Learning to Data Mining
– Shin, Yun, et al.
- 2000
|
|
4
|
A search technique for rule extraction from trained neural networks
– Krishnan, Sivakumar, et al.
- 1999
|
|
4
|
An Adaptive Neural Network Approach to Fault Diagnosis
– He, Zhou, et al.
- 1999
|
|
3
|
Claris, “Unsupervised neural network approach to medical data mining techniques
– Shalvi, De
- 1998
|
|
2
|
Data Mining Techniques. Tutorial at the 1996
– Han
- 1996
|
|
2
|
An Overview of Database Mining Technologies
– Kero, Russell, et al.
- 1995
|
|
2
|
Data Mining Applied to Transformer Oil Analysis Data
– Esp, Carrillo, et al.
- 1998
|
|
2
|
Engineering Semantic Evaluation of Decision Rules
– Arciszewski
- 1997
|
