A fuzzy-rough sets based compact rule induction method for classifying hybrid data

Authors:
Yang Liu;Qinglei Zhou;Elisabeth Rakus-Andersson;Guohua Bai
Affiliations:
School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China,School of Engineering, Blekinge Institute of Technology, Karlskrona, Blekinge, Sweden;School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China;School of Applied Mathematics, Blekinge Institute of Technology, Karlskrona, Blekinge, Sweden;School of Engineering, Blekinge Institute of Technology, Karlskrona, Blekinge, Sweden
Venue:
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Year:
2012

Citing 10
Cited 0

Induction of Decision Trees

Machine Learning
CAIM Discretization Algorithm

IEEE Transactions on Knowledge and Data Engineering
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation

Pattern Recognition
Knowledge acquisition for decision support systems on an electronic assembly line

Expert Systems with Applications: An International Journal
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

Applied Soft Computing
An Extended Comparison of Six Approaches to Discretization - A Rough Set Approach

Fundamenta Informaticae - Fundamentals of Knowledge Technology
On the compact computational domain of fuzzy-rough sets

Pattern Recognition Letters
An empirical comparison of rule sets induced by LERS and probabilistic rough classification

RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Mining numerical data – a rough set approach

Transactions on Rough Sets XI
Highly scalable and robust rule learner: performance evaluation and comparison

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rule induction plays an important role in knowledge discovery process. Rough set based rule induction algorithms are characterized by excellent accuracy, but they lack the abilities to deal with hybrid attributes such as numeric or fuzzy attributes. In real-world applications, data usually exists with hybrid formats, and thus a unified rule induction algorithm for hybrid data learning is desirable. We firstly model different types of attributes in equivalence relationship, and define the key concepts of block, minimal complex and local covering based on fuzzy rough sets model, then propose a rule induction algorithm for hybrid data learning. Furthermore, in order to estimate performance of the proposed method, we compare it with state-of-the-art methods for hybrid data learning. Comparative studies indicate that rule sets extracted by this method can not only achieve comparable accuracy, but also get more compact rule sets. It is therefore concluded that the proposed method is effective for hybrid data learning.