A new iterative model to simplify the knowledge extracted on a fuzzy rule-based learning algorithm

In system identification process often a predetermined set of features is used. However, in many cases it is difficult to know a priori whether the selected features were really the more appropriate ones. This is the reason why the feature construction techniques have been very interesting in many applications. Thus, the current proposal introduces the use of these techniques in order to improve the description of fuzzy rule-based systems. In particular, the idea is to include feature construction in a genetic learning algorithm. The construction of attributes in this study will be restricted to the inclusion of functions defined on the initial attributes of the system. Since the number of functions and the number of attributes can be very large, a filter model, based on the use of information measures, is introduced. In this way, the genetic algorithm only needs to explore the particular new features that may be of greater interest to the final identification of the system. In order to manage the knowledge provided by the new attributes based on the use of functions we propose a new model of rule by extending a basic learning fuzzy rule-based model. Finally, we show the experimental study associated with this work.

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An iterative strategy for feature construction on a fuzzy rule-based learning algorithm

2011 11th International Conference on Intelligent Systems Design and Applications ◽

10.1109/isda.2011.6121828 ◽

2011 ◽

Author(s):

David Garcia ◽

Antonio Gonzalez ◽

Raul Perez

Keyword(s):

Learning Algorithm ◽

Fuzzy Rule ◽

Feature Construction ◽

Rule Based

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A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction

Advances in Data Mining. Applications and Theoretical Aspects - Lecture Notes in Computer Science ◽

10.1007/978-3-319-41561-1_14 ◽

2016 ◽

pp. 183-196

Author(s):

Bingquan Huang ◽

Ying Huang ◽

Chongcheng Chen ◽

M. -T. Kechadi

Keyword(s):

Learning Algorithm ◽

Fuzzy Rule ◽

Churn Prediction ◽

Rule Based ◽

Customer Churn ◽

Customer Churn Prediction

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ON DESIGNING FUZZY RULE-BASED MULTICLASSIFICATION SYSTEMS BY COMBINING FURIA WITH BAGGING AND FEATURE SELECTION

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488511007155 ◽

2011 ◽

Vol 19 (04) ◽

pp. 589-633 ◽

Cited By ~ 23

Author(s):

KRZYSZTOF TRAWIŃSKI ◽

OSCAR CORDÓN ◽

ARNAUD QUIRIN

Keyword(s):

Feature Selection ◽

Learning Algorithm ◽

Rule Learning ◽

Fuzzy Rule ◽

Fuzzy Classification ◽

Classification Rule ◽

High Dimensional ◽

Global Approach ◽

Rule Based ◽

Exhaustive Study

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature selection to design a final FURIA-based fuzzy multiclassifier dealing with high dimensional data. Several parameter settings for the global approach are tested when applied to twenty one popular UCI datasets. The results obtained show that FURIA-based fuzzy multiclassifiers outperform the single FURIA classifier and are competitive with C4.5 multiclassifiers and random forests.

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Cost-Sensitive Classification for Medical Diagnosis

Encyclopedia of Healthcare Information Systems ◽

10.4018/978-1-59904-889-5.ch040 ◽

2008 ◽

pp. 297-302 ◽

Cited By ~ 1

Author(s):

Gerald Schaefer ◽

Tomoharu Nakashima ◽

Yasuyuki Yokota

Keyword(s):

Pattern Classification ◽

Medical Diagnosis ◽

Learning Algorithm ◽

Fuzzy Rule ◽

Breast Cancer Dataset ◽

Classification Problems ◽

Rule Based ◽

Cost Sensitive Classification ◽

Minimisation Problem ◽

Rule Based Classifier

In this article, we present a cost-sensitive approach to medical diagnosis based on fuzzy rule-based classification (Schaefer, Nakashima, Yokota, & Ishibuchi, 2007). While fuzzy rule-based systems have been mainly employed for control problems (Lee, 1990) more recently they have also been applied to pattern classification problems (Ishibuchi & Nakashima, 1999; Nozaki, Ishibuchi, & Tanaka, 1996). We modify a fuzzy rule-based classifier to incorporate the concept of weight which can be considered as the cost of an input pattern being misclassified. The pattern classification problem is thus reformulated as a cost minimisation problem. Based on experimental results on the Wisconsin breast cancer dataset, we demonstrate the efficacy of our approach. We also show that the application of a learning algorithm can further improve the classification performance of our classifier.

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