A fuzzy rule induction algorithm for discovering classification rules

2016 ◽  
Vol 30 (6) ◽  
pp. 3067-3085 ◽  
Author(s):  
Ashraf A. Afify
Keyword(s):  
Fuzzy Rule ◽  
Rule Induction ◽  
Classification Rules

A novel algorithm for fuzzy rule induction in data mining

2013 ◽  
Vol 228 (5) ◽  
pp. 877-895 ◽  
Author(s):  
AA Afify
Keyword(s):  
Data Mining ◽  
Predictive Accuracy ◽  
Fuzzy Rule ◽  
Rule Induction ◽  
Fuzzy Classification ◽  
Automatic Identification ◽  
Classification Rules ◽  
Fuzzy Techniques ◽  
Fuzzy Learning ◽  
Novel Algorithm

Rule induction as a method of constructing classifiers is of particular interest to data mining because it generates models in the form of If-Then rules which are more expressive and easier for humans to comprehend and check. Several induction algorithms have been developed to learn classification rules. However, most of these algorithms are based on ‘crisp’ data and produce ‘crisp’ models. This paper presents FuzzySRI, a novel algorithm based on the techniques of fuzzy sets and fuzzy logic for inducing fuzzy classification rules. The algorithm possesses the clear knowledge representation capability of rule induction methods and the ability of fuzzy techniques to handle vague information. Experimental results show that FuzzySRI can outperform other fuzzy and non-fuzzy learning systems in terms of predictive accuracy, comprehensibility, and computational efficiency. It is also shown that FuzzySRI can be successfully applied to an industrial application concerning the automatic identification of machine faults.

scholarly journals FURIA: an algorithm for unordered fuzzy rule induction

2009 ◽  
Vol 19 (3) ◽  
pp. 293-319 ◽  
Author(s):  
Jens Hühn ◽  
Eyke Hüllermeier
Keyword(s):  
Fuzzy Rule ◽  
Rule Induction

A fuzzy rule induction method using genetic algorithm

1996 ◽  
Vol 18 (2-3) ◽  
pp. 135-145 ◽  
Author(s):  
Toshio Tsuchiya ◽  
Tatsushi Maeda ◽  
Yukihiro Matsubara ◽  
Mitsuo Nagamachi
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rule ◽  
Rule Induction ◽  
Induction Method

Fuzzy rule induction in a set covering framework

2006 ◽  
Vol 14 (1) ◽  
pp. 93-110 ◽  
Author(s):  
I. Cloete ◽  
J. van Zyl
Keyword(s):  
Fuzzy Rule ◽  
Rule Induction ◽  
Set Covering

Study of population dynamics of soybean semi-looper Gesonia gemmaSwinhoe by using rule induction model in Maharashtra, India

2017 ◽  
Author(s):  
J. Cruz Antony ◽  
M. Pratheepa
Keyword(s):  
Population Dynamics ◽  
Control Strategy ◽  
Rule Induction ◽  
Maximum Temperature ◽  
Classification Rules ◽  
Target Feature ◽  
Sequential Covering ◽  
Heavy Damage ◽  
Induction Model ◽  
Rainy Days

Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 rule induction) has been proposed for rule induction model to generate a list of classification rules with target feature (G. gemma population) and the independent abiotic features. The classification rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this rule induction model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.

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