Enhanced cAntMinerPB Algorithm for Induction of Classification Rules using Ant Colony Approach

Rajpiplawala, Safeya; Singh, Dheeraj K.

doi:10.9790/0661-16326372

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Other studies have determined the class consequent in advance and then constructed the rule for this class, allocating different types of pheromone for each class [49]. Different quality functions, and each ant in the colony must select the quality functions before the rule construction process, as proposed by Rajpiplawala & Singh (2014) [50]. The Laplace correction heuristic function was proposed to replace the original heuristic function in the Ant-Miner algorithm [51].…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Al-Behadili

Sagban

Ku-Mahamud

2020

IJEEI

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Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.

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Section: Related Workmentioning

confidence: 99%

“…Addition of terms will stop when all the attributes have been visited, or any additional term inserted into the construction rule would cover less than the allowed number of minimum cases per rule. A term is selected using the Ant-Miner probability [50].…”

Section: The Proposed Acs-antminer Classification Algorithmmentioning

confidence: 99%

Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Al-Behadili

Sagban

Ku-Mahamud

2020

IJEEI

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“…The post-pruning procedure is then terminated to avoid the underfitting rule. The class value of the dataset can potentially change during this procedure, because the majority of classes in the instances covered by the pruned rule might be changed compared with those covered by original rules [9,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Furthermore, the algorithms proposed by [33][34][35][36][37][38] still use the same traditional procedure to prune the rule, but they introduce a new fitness function to test quality.…”

Section: A Post-pruning Technique In Ant-miner Variantsmentioning

confidence: 99%

Rule pruning techniques in the ant-miner classification algorithm and its variants: A review

Al-Behadili

Ku-Mahamud

Sagban

2018

2018 IEEE Symposium on Computer Applications &Amp; Industrial Electronics (ISCAIE)

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Rule-based classification is considered an important task of data classification. The ant-mining rule-based classification algorithm, inspired from the ant colony optimization algorithm, shows a comparable performance and outperforms in some application domains to the existing methods in the literature. One problem that often arises in any rule-based classification is the overfitting problem. Rule pruning is a framework to avoid overfitting. Furthermore, we find that the influence of rule pruning in ant-miner classification algorithms is equivalent to that of local search in stochastic methods when they aim to search for more improvement for each candidate solution. In this paper, we review the history of the pruning techniques in ant-miner and its variants. These techniques are classified into post-pruning, pre-pruning and hybrid-pruning. In addition, we compare and analyse the advantages and disadvantages of these methods. Finally, future research direction to find new hybrid rule pruning techniques are provided.

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A rule-based fuzzy ant colony improvement (ACI) approach for automated disease diagnoses

Bagla

Kumar

2023

Multimed Tools Appl

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Enhanced cAntMinerPB Algorithm for Induction of Classification Rules using Ant Colony Approach

Abstract: : Mining classification rules from data is a key mission of data mining and is getting great attention in recent years. Rule induction is a method used in data mining

Cited by 4 publications

References 11 publications

Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Rule pruning techniques in the ant-miner classification algorithm and its variants: A review

A rule-based fuzzy ant colony improvement (ACI) approach for automated disease diagnoses

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