A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Abstract

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

“…Some procedures which often deal with fuzzy inclusion or entropy measurements concern feature selection, fuzzy classification, fuzzy controllers, fuzzy rules and similarity measures. There are several presentations of this kind (e.g., [112][113][114][115][116][117][118][119] are a few recent) and it would be extremely useful if a certain part of our work could be connected with (or even included in) some of these studies. This would be a significant aid to our research and we could better comprehend the attributes and the performance of these measures.…”

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

“…As a probability function, the hyperbolic tangent was employed. In [37], binary gravitational search with S-shaped and V-shaped functions was used to select features for a fuzzy classifier; in [38], for a classifier based on k-nearest neighbors.…”

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

“…Thus, the number of terms for each feature is equal to the number of classes. The pseudo code of the algorithm for generating rule base by extreme feature values is provided in [32].…”

“…Then the same calculations occur as in the binary version of the algorithm, but elements of each vector  are updated by adding to their current speed value. A detailed description of the algorithms and their pseudo-codes are presented by us in [32].…”