Intelligent Hypothermia Care System using Ant Colony Optimization for Rules Prediction

Al-Behadili, Hayder Naser Khraibet

doi:10.29196/jub.v26i2.473

Cited by 6 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different institutions and companies consider it as the most crucial opportunity to raise revenue. Data mining is widely used in various fields, such as medicine, science, recognition, business, and engineering [1]. In data mining, two types of learning are available: supervised techniques and unsupervised learning approaches [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Al-Behadili

Ku-Mahamud

Sagban

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

Self Cite

View full text Add to dashboard Cite

Ant colony optimization (ACO) is a well-known algorithm from swarm intelligence that plays an essential role in obtaining rich solutions to complex problems with wide search space. ACO is successfully applied to different application problems involving rules-based classification through an ant-miner classifier. However, in the ant-miner classifier, rule-pruning suffers from the problem of nesting effect origins from the method of greedy Sequential Backward Selection (SBS) in term selection, thereby depriving the opportunity of obtaining a good pruned rule by adding/removing the terms during the pruning process. This paper presents an extension to the Ant-Miner, namely the genetic algorithm Ant-Miner (GA-Ant Miner), which incorporates the use of GA as a key aspect in the design and implementation of a new rule pruning technique. This pruning technique consists of three fundamental procedures: an initial population Ant-Miner, crossover to prune the rule, and mutation to diversify the pruned classification rule. The GA-Ant Miner performance is tested and compared with the most related ant-mining classifiers, including the original Ant-Miner, ACO/ PSO2, TACO-Miner, CAnt-Miner, and Ant-Miner with a hybrid pruner, across various public available UCI datasets. These datasets are varied in terms of instance number, feature size, class number, and the application domains. Overall, the performance results indicate that the GA-Ant Miner classifier outperforms the other five classifiers in the classification accuracy and model size. Furthermore, the experimental results using statistical test prove that GA-Ant Miner is the best classifier when considering the multi objectives (i.e., accuracy and model size ranks).

show abstract

Section: Introductionmentioning

confidence: 99%

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Al-Behadili

Ku-Mahamud

Sagban

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of computer and artificial intelligence has improved the health care system, which led to an increase in the demand for intelligent and the discover knowledge in modern medicine practices [7], [8]. The process of data collection of diseases leads to the large volume of data stored in medical databases.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the use of artificial intelligence techniques to the medical databases contributes to enhancing the process of diagnosing and detecting diseases such as works related to breast cancer [9],liver tumor [10]and malaria fever [11]. Over the past few years, there were several methods proposed and applied for diagnosing and detecting the occurrence of cervical cancer in medical database including principal component analysis (PCA), linear regression (LR), particle swarm optimization (PSO), support vector machine (SVM), Artificial Neural Network (ANN), fuzzy positivistic C-means clustering, genetic algorithm (GA), hierarchical decision approach (HDA), texture analysis, Artificial Bee Colony (ABC), Simulated Annealing (SA), Firefly (FA) and Ant Colony Optimization (ACO) [8], [12], [21]- [24], [13]- [20].…”

Section: Introductionmentioning

confidence: 99%

Cervical Cancer Diagnosis System Using Ant-Miner for Managing the Knowledge in Medical Database

et.al¹

2021

TURCOMAT

View full text Add to dashboard Cite

The fourth most frequent cause of cancer death in women is cervical cancer. No sign can be observed in the early stages of the disease. In addition, cervical cancer diagnosis methods used in health centers are time-consuming and costly. Data classification has been widely applied in the diagnosis of cervical cancer for knowledge acquisition. However, none of the existing intelligent methods are comprehensible, and they look like a black box to clinicians. In this paper, an ant colony optimization-based classification algorithm, Ant-Miner is applied to analyze the cervical cancer data set. The cervical cancer data set used was obtained from the repository of the University of California, Irvine. The proposed algorithm outperforms the previous approach, support vector machine, in the same domain, in terms of the better result of classification accuracy. The proposed method is implemented as an engine in a prototype system named as the cervical cancer detection system. Evaluation of the prototype system demonstrates a good result on its usability and functionality.

show abstract

“…The Ant-Miner generates rules in the form of if-then rules that are considered simple and comprehensible knowledge representor. Examples of Ant-Miner applications include medical diagnosis, bankruptcy determination and stock price prediction (Al-Behadili, 2018;Ripon, 2019).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Ant Colony Optimization and Genetic Algorithm for Rule Induction

Al-Behadili¹,

Ku-Mahamud²,

Sagban³

2020

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

In this study, a hybrid rule-based classifier namely, ant colony optimization/genetic algorithm ACO/GA is introduced to improve the classification accuracy of Ant-Miner classifier by using GA. The Ant-Miner classifier is efficient, useful and commonly used for solving rulebased classification problems in data mining. Ant-Miner, which is an ACO variant, suffers from local optimization problem which affects its performance. In our proposed hybrid ACO/GA algorithm, the ACO is responsible for generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem. The performance of the proposed classifier was tested against other existing hybrid ant-mining classification algorithms namely, ACO/SA and ACO/PSO2 using classification accuracy, the number of discovered rules and model complexity. For the experiment, the 10-fold cross-validation procedure was used on 12 benchmark datasets from the University California Irwine machine learning repository. Experimental results show that the proposed hybridization was able to produce impressive results in all evaluation criteria.

show abstract

Intelligent Hypothermia Care System using Ant Colony Optimization for Rules Prediction

Cited by 6 publications

References 5 publications

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Cervical Cancer Diagnosis System Using Ant-Miner for Managing the Knowledge in Medical Database

Hybrid Ant Colony Optimization and Genetic Algorithm for Rule Induction

Contact Info

Product

Resources

About