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.

Risk Budgeted Portfolio Optimization Using an Extended Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 3 (4) ◽  
pp. 25-42 ◽  
Author(s):  
G. A. Vijayalakshmi Pai
Keyword(s):  
Ant Colony Optimization ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Experimental Studies ◽  
Optimization Methods ◽  
Investment Strategy ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Exchange Data

Risk Budgeted portfolio optimization problem centering on the twin objectives of maximizing expected portfolio return and minimizing portfolio risk and incorporating the risk budgeting investment strategy, turns complex for direct solving by classical methods triggering the need to look for metaheuristic solutions. This work explores the application of an extended Ant Colony Optimization algorithm that borrows concepts from evolution theory, for the solution of the problem and proceeds to compare the experimental results with those obtained by two other Metaheuristic optimization methods belonging to two different genres viz., Evolution Strategy with Hall of Fame and Differential Evolution, obtained in an earlier investigation. The experimental studies have been undertaken over Bombay Stock Exchange data set (BSE200: July 2001-July 2006) and Tokyo Stock Exchange data set (Nikkei225: July 2001-July 2006). Data Envelopment Analysis has also been undertaken to compare the performance of the technical efficiencies of the optimal risk budgeted portfolios obtained by the three approaches.

scholarly journals Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 188 ◽  
Author(s):  
Xiangyin Zhang ◽  
Yuying Xue ◽  
Xingyang Lu ◽  
Songmin Jia
Keyword(s):  
Differential Evolution ◽  
Ant Colony Optimization ◽  
Structure Learning ◽  
Heuristic Algorithms ◽  
Evolutionary Process ◽  
Ant Colony ◽  
Training Data ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Optimal Network

Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy.

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.

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