scholarly journals Application of the Gravitational Search Algorithm for Constructing Fuzzy Classifiers of Imbalanced Data

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1458 ◽  
Author(s):  
Marina Bardamova ◽  
Ilya Hodashinsky ◽  
Anton Konev ◽  
Alexander Shelupanov
Keyword(s):  
Extreme Values ◽  
Search Algorithm ◽  
Geometric Mean ◽  
Gravitational Search Algorithm ◽  
Imbalanced Data ◽  
Fuzzy Classification ◽  
Quality Metric ◽  
Fuzzy Classifiers ◽  
Minimum Number ◽  
Gravitational Search

The presence of imbalance in data significantly complicates the classification task, including fuzzy systems. Due to a large number of instances of bigger classes, instances of smaller classes are not recognized correctly. Therefore, additional tools for improving the quality of classification are required. The most common methods for handling imbalanced data have several disadvantages. For example, methods for generating additional instances of minority classes can worsen classification if there is a strong overlap of instances from different classes. Methods that directly modify the fuzzy classification algorithm lead to a decline in the interpretability of the model. In this paper, we study the efficiency of the gravitational search algorithm in the tasks of selecting the features and tuning the term parameters for fuzzy classifiers of imbalanced data. We consider only data with two classes and apply the algorithm based on extreme values of classes to construct models with a minimum number of rules. In addition, we propose a new quality metric based on the sum of the overall accuracy and the geometric mean with the presence of a priority coefficient between them.

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.

scholarly journals Fuzzy Classification with Comprehensive Learning Gravitational Search Algorithm in Breast Tumor Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 2688-2694 ◽  
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Classification ◽  
Fuzzy Classifier ◽  
Data Set ◽  
Cancer Data ◽  
Good Ability ◽  
Gravitational Search ◽  
Heuristics Algorithm ◽  
The University

The research paper herewith presents an effectual diagnosis classification system using fuzzy classifier and a very efficient heuristics algorithm comprehensive learning gravitational search algorithm (CLGSA) which has a good ability to search and finding optimal solutions. The effectiveness of the proposed model is estimating on Wisconsin breast cancer data set available in the UCI Machine learning source in the University of California, Irvine. We testify the data over the parameters of classification of accurateness, sensitivity as well as specificity with a much better and more responsive 10-fold cross validation method; which is considered as a reliable diagnostics model in the medical field. Experiment results have clearly shown that the proposed approach will turn out to be a calculative and decisive medium for cancer detection in the field of medicine

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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