Software quality estimation based on measured attributes from previous similar products is an active field of research. Such estimation models must inevitably handle imprecision and uncertainty and hence soft computing techniques are gaining popularity. This paper presents a Fuzzy rule based classifier for software quality data and its performance is compared with Bayesian classifier. The fuzzy...

Fuzzy rule-based systems suffer from the so-called curse of dimensionality when applied to high complexity datasets, which consist of a large number of variables and/or examples. Fuzzy rule-based classifier ensembles have shown to be a good approach to deal with this kind of problems. In this contribution, we would like to take one step forward and extend this approach with two variants of rand...

The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...

The need to deduce interesting and valuable information from large, complex, information-rich data sets is common to many research fields. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category in a comprehensible way. Besides the classical approaches, many rule mining approaches use biologicallyinspired algorithms such as evolutionary algorithms and swarm int...

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets ...

Fuzzy rule-based systems have shown a high capability of knowledge extraction and representation when modeling complex, nonlinear classification problems. However, they suffer from the so-called curse of dimensionality when applied to high dimensional datasets, which consist of a large number of variables and/or examples. Multiclassification systems have shown to be a good approach to deal with...

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...

In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strateg...