Fuzzy rule-based hyperspectral band selection algorithm with ant colony optimization

2022 ◽  
Author(s):  
Aditi Roy Chowdhury ◽  
Joydev Hazra ◽  
Kousik Dasgupta ◽  
Paramartha Dutta
Keyword(s):  
Ant Colony Optimization ◽  
Fuzzy Rule ◽  
Ant Colony ◽  
Band Selection ◽  
Selection Algorithm ◽  
Rule Based

scholarly journals Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

2020 ◽  
Vol 10 (6) ◽  
pp. 6655
Author(s):  
Rafid Sagban ◽  
Haydar A. Marhoon ◽  
Raaid Alubady
Keyword(s):  
Health Care ◽  
Cervical Cancer ◽  
Feature Selection ◽  
Ant Colony Optimization ◽  
Diagnostic Methods ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Rule Based ◽  
Cancer Data

Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.

scholarly journals Optimal Design of Adaptive Neuro-Fuzzy Inference System Using PSO And Ant Colony Optimization For Estimation of Uncertain Observed Values

2021 ◽  
Author(s):  
Mahdi Danesh ◽  
Sedighe Danesh
Keyword(s):  
Ant Colony Optimization ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Ant Colony ◽  
Fuzzy Regression ◽  
Machining Process ◽  
Estimation Methods ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Parameter

Abstract This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC\({O}_{R}\). In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.

Feature Selection Based on Ant Colony Optimization for Image Classification

2013 ◽  
Vol 319 ◽  
pp. 337-342
Author(s):  
Li Tu ◽  
Li Zhi Yang
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Image Data ◽  
Ant Colony ◽  
Data Sets ◽  
Classification Rules ◽  
Selection Algorithm ◽  
Artificial Ants

In this paper, a feature selection algorithm based on ant colony optimization (ACO) is presented to construct classification rules for image classification. Most existing ACO-based algorithms use the graph with O(n2) edges. In contrast, the artificial ants in the proposed algorithm FSC-ACO traverse on a feature graph with only O(n) edges. During the process of feature selection, ants construct the classification rules for each class according to the improved pheromone and heuristic functions. FSC-ACO improves the qualities of rules depend on the classification accuracy and the length of rules. The experimental results on both standard and real image data sets show that the proposed algorithm can outperform the other related methods with fewer features in terms of speed, recall and classification accuracy.

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