A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm

Chan, Allen; Freitas, Alex A.

doi:10.1007/11740698_3

Cited by 32 publications

(26 citation statements)

References 10 publications

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“…This measure is simple and quantified. Chan and Freitas also measured rule comprehensibility by the average number of terms in the discovered rules [8] but they did not consider the number of input attributes. The above three measures help performance evaluation in terms of axiom confidence, capacity, and comprehensibility.…”

Section: Methodsmentioning

confidence: 99%

“…They described how some concepts of machine learning matched nicely with MVLS and showed how MVLS outperformed both C4.5, the widely used classification algorithm and Espresso, an industry standard logic minimization tool. Also, iterative mining for rules with constrained antecedents was reported [8]. This approach was an iterative algorithm that could exploit mining information gained in previous steps to efficiently answer subsequent queries.…”

Section: Related Researchmentioning

confidence: 99%

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Constructing a User Preference Ontology for Anti-spam Mail Systems

Kim

Dou

Liu

et al. 2007

Advances in Artificial Intelligence

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Abstract. The judgment that whether an email is spam or non-spam may vary from person to person. Different individuals can have totally different responses to the same email based on their preferences. This paper presents an innovative approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based approaches. We build a user preference ontology to formally represent the important concepts and rules derived from a data mining process. Then we use an inference engine that utilizes the knowledge to predict the user's action on new incoming emails. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules. Experimental results showed that our user preference based architecture achieved good performance and the rules derived from the architecture and the optimization method have better quality in terms of comprehensibility.

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Section: Methodsmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Constructing a User Preference Ontology for Anti-spam Mail Systems

Kim

Dou

Liu

et al. 2007

Advances in Artificial Intelligence

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“…In the latter case, this is achieved by letting the ants minimize a function consisting of a set of decision variables corresponding to the neuron parameter weights. Furthermore, there is a multitude of hybrid ACO variants for Bayesian networks [3], multi-net classifiers [17], and rule pruning [2].…”

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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“…pheromone update and rule construction procedures [12]; pruning procedure [13]. More elaborated variations are able to cope with both nominal and continuous attributes [14], [15], overcoming Ant-Miner's limitation of being able to cope only with nominal attributes; provide a new construction graph exploiting the difference between nominal and ordinal attributes, where ants first select the class value predicted by the rule, allowing the algorithm to use class-specific heuristic information [16];…”

Section: To Appear In Ieee Transactions On Evolutionary Computation Cmentioning

confidence: 99%

A New Sequential Covering Strategy for Inducing Classification Rules With Ant Colony Algorithms

Otero

Freitas

Johnson

2013

IEEE Trans. Evol. Computat.

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Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction-i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using eighteen publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms.

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A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm

Cited by 32 publications

References 10 publications

Constructing a User Preference Ontology for Anti-spam Mail Systems

Constructing a User Preference Ontology for Anti-spam Mail Systems

Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons

A New Sequential Covering Strategy for Inducing Classification Rules With Ant Colony Algorithms

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