Improving the precision of CBIR systems by feature selection using binary gravitational search algorithm

2012 ◽  
Author(s):  
Esmat Rashedi ◽  
Hossein Nezamabadi-pour
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search

Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection

2020 ◽  
Vol 93 ◽  
pp. 106341 ◽  
Author(s):  
Ritam Guha ◽  
Manosij Ghosh ◽  
Akash Chakrabarti ◽  
Ram Sarkar ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search

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

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

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.

Quantum-inspired binary gravitational search algorithm to recognize the facial expressions

2020 ◽  
Vol 31 (10) ◽  
pp. 2050138
Author(s):  
Yogesh Kumar ◽  
Shashi Kant Verma ◽  
Sandeep Sharma
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Expression Recognition ◽  
Side View ◽  
Selection Approach ◽  
Gravitational Search ◽  
Feature Selection Approach

This paper addresses an autonomous facial expression recognition system using the feature selection approach of the Quantum-Inspired Binary Gravitational Search Algorithm (QIBGSA). The detection of facial features completely depends upon the selection of precise features. The concept of QIBGSA is a modified binary version of the gravitational search algorithm by mimicking the properties of quantum mechanics. The QIBGSA approach reduces the computation cost for the initial extracted feature set using the hybrid approach of Local binary patterns with Gabor filter method. The proposed automated system is a sequential system with experimentation on the image-based dataset of Karolinska Directed Emotional Faces (KDEF) containing human faces with seven different emotions and different yaw angles. The experiments are performed to find out the optimal emotions using the feature selection approach of QIBGSA and classification using a deep convolutional neural network for robust and efficient facial expression recognition. Also, the effect of variations in the yaw angle (front to half side view) on facial expression recognition is studied. The results of the proposed system for the KDEF dataset are determined in three different cases of frontal view, half side view, and combined frontal and half side view images. The system efficacy is analyzed in terms of recognition rate.

Fuzzy Optimized Gravitational Search Algorithm for Disease Prediction

2020 ◽  
Vol 11 (3) ◽  
pp. 89-103
Author(s):  
Utkarsh Yadav ◽  
Twishi Tyagi ◽  
Sushama Nagpal
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Parameter Tuning ◽  
Disease Prediction ◽  
Search Optimization ◽  
Minimum Number ◽  
Gravitational Search ◽  
Nearest Neighbour Classifier

In this article, fuzzy logic and gravitational search algorithms have been amalgamated and explored for feature selection in the automated prediction of diseases. The gravitational search algorithm has been used for search optimization while fuzzy logic had been used for its parameter tuning. Feature selection has been considered as a dual objective problem in the article, i.e. selecting minimum number of features without compromising the accuracy of classification, which is performed using K-Nearest Neighbour classifier. The improved algorithm has been tested with various publicly available medical datasets to analyse its effectiveness. The results indicate that the approach not only reduces the feature set by an average of 67.66% but also increases the accuracy by an average of 12%. Further, the results have also been compared with the prior work wherein the feature selection has been done using other evolutionary techniques. It is observed that the proposed approach is able to generate better results in most of the cases.

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