Modified Ant Colony Optimization Algorithm (MAnt-Miner) for Classification Rule Mining

Hota, Sarbeswara; Satapathy, Pranati; Jagadev, Alok Kumar

doi:10.1007/978-81-322-2012-1_28

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…The rule based classifiers [62][63][64] construct graphs by letting the ants walk with preference towards common occurring examples so that strong pheromone trails are used as rules in the classifier, or a set of IF-THEN rules [65]. The most notable rule based ACO classifiers are probably the AntMiner series [66] including: AntMiner [63,67], AntMiner2 [63], AntMiner3 [62], AntMiner+ [64] and new variantssuch as MAnt-Miner [68] and TACO-miner [69]. All the aforementioned AntMiner variants rely on the idea of letting the ant walk "on" examples so that the pheromone trails can yield usable rules.…”

Section: Aco For Classificationmentioning

confidence: 99%

Distributed learning automata-based scheme for classification using novel pursuit scheme

Goodwin

Yazidi

2020

Appl Intell

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Learning Automata (LA) is a popular decision making mechanism to "determine the optimal action out of a set of allowable actions" [1]. The distinguishing characteristic of automata-based learning is that the search for the optimising parameter vector is conducted in the space of probability distributions defined over the parameter space, rather than in the parameter space itself [2]. Recently, Goodwin and Yazidi pioneered the use of Ant Colony Optimisation (ACO) for solving classification problems [3]. In this paper, we propose a novel classifier based on the theory of LA. The classification problem is formulated as a deterministic optimization problem involving a team of LA that operate collectively to optimize an objective function.Although many LA algorithms have been devised in the literature, those LA schemes are not able to solve deterministic optimization problems as they suppose that the environment is stochastic. In this paper, we develop a novel pursuit LA which can be seen as the counterpart of the family of pursuit LA developed for stochastic environments [4]. While classical pursuit LA are able to pursue the action with the highest reward estimate, our pursuit LA rather pursues the collection of actions that yield the highest performance. The theoretical analysis of the pursuit scheme does not follow classical LA proofs and can pave the way towards more schemes where LA can be applied to solve deterministic optimization problems.When applied to classification, the essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a

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Section: Aco For Classificationmentioning

confidence: 99%

Distributed learning automata-based scheme for classification using novel pursuit scheme

Goodwin

Yazidi

2020

Appl Intell

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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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“…Probably, the most notable rule based ACO classifiers are the AntMiner series [7] including: AntMiner [14], AntMiner2, AntMiner3, AntMiner+ [12] and new variantssuch as MAnt-Miner [6]. All the aforementioned AntMiner variants rely on the idea of letting the ant walk "on" examples so that the pheromone trails can yield usable rules.…”

Section: Aco For Classificationmentioning

confidence: 99%

Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons

Goodwin

Yazidi

2016

Lecture Notes in Computer Science

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Abstract. The application of Ant Colony Optimization to the field of classification has mostly been limited to hybrid approaches which attempt at boosting the performance of existing classifiers (such as Decision Trees and Support Vector Machines (SVM)) -often through guided feature reductions or parameter optimizations.In this paper we introduce PolyACO: A novel Ant Colony based classifier operating in two dimensional space that utilizes ray casting. To the best of our knowledge, our work is the first reported Ant Colony based classifier which is non-hybrid, in the sense, that it does not build on any legacy classifiers. The essence of the scheme is to create a separator in the feature space by imposing ant-guided random walks in a grid system. The walks are self-enclosing so that the ants return back to the starting node forming a closed classification path yielding a many edged polygon. Experimental results on both synthetic and real-life data show that our scheme is able to perfectly separate both simple and complex patterns, without utilizing "kernel tricks" and outperforming existing classifiers, such as polynomial and linear SVM. The results are impressive given the simplicity of PolyACO compared to other approaches such as SVM.

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Modified Ant Colony Optimization Algorithm (MAnt-Miner) for Classification Rule Mining

Cited by 4 publications

References 2 publications

Distributed learning automata-based scheme for classification using novel pursuit scheme

Distributed learning automata-based scheme for classification using novel pursuit scheme

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

Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons

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