Ant Colony Optimization for Feature Selection Involving Effective Local Search

2011 ◽  
Vol 15 (6) ◽  
pp. 671-680
Author(s):  
Md. Monirul Kabir ◽  
◽  
Md. Shahjahan ◽  
Kazuyuki Murase ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Search Strategy ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Exploration And Exploitation ◽  
Algorithm Performance

This paper proposes an effective algorithm for feature selection (ACOFS) that uses a global Ant Colony Optimization algorithm (ACO) search strategy. To make ACO effective in feature selection, our proposed algorithm uses an effective local search in selecting significant features. The novelty of ACOFS lies in its effective balance between ant exploration and exploitation using new pheromone update and heuristic information computation rules to generate a subset of a smaller number of significant features. We evaluate algorithm performance using seven real-world benchmark classification datasets. Results show that ACOFS generates smaller subsets of significant features with improved classification accuracy.

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 Semi-supervised associative classification using ant colony optimization algorithm

2021 ◽  
Vol 7 ◽  
pp. e676
Author(s):  
Hamid Hussain Awan ◽  
Waseem Shahzad
Keyword(s):  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Ant Colony ◽  
Associative Classification ◽  
Ant Colony Optimization Algorithm ◽  
Main Ingredient ◽  
Novel Approach ◽  
Training Approach ◽  
Classification Tasks ◽  
Class Labels

Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

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.

scholarly journals Ant-Set: A Subset-Oriented Ant Colony Optimization Algorithm for the Set Covering Problem

2020 ◽  
Vol 26 (2) ◽  
pp. 293-316
Author(s):  
Murilo Falleiros Lemos Schmitt ◽  
Mauro Mulati ◽  
Ademir Constantino ◽  
Fábio Hernandes ◽  
Tony Hild
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Ant Colony Optimization Algorithm ◽  
The Impact

This paper proposes an algorithm for the set covering problem based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Set, which uses a lineoriented approach and a novelty pheromone manipulation based on the connections between components of the construction graph, while also applying a local search. The algorithm is compared with other ACO-based approaches. The results obtained show the effectiveness of the algorithm and the impact of the pheromone manipulation.

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