A coevolutionary algorithm for rules discovery in data mining

Tan, Kay Chen; Yu, Qiang; Ang, Jovina

doi:10.1080/00207720600879641

Cited by 56 publications

(15 citation statements)

References 44 publications

(48 reference statements)

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“…In the case of repeating pairwise comparisons, there is an associated error that grows agreeing with the number of comparisons done, called the family-wise error rate (FWER), defined as the probability of at least one error in the family of hypotheses. For solving this problem, some authors use the Bonferroni correction for applying paired t-test in their works (Tan et al 2006;Bacardit 2004).…”

Section: Case Studies Of the Normality Propertymentioning

confidence: 99%

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

et al. 2008

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The experimental analysis on the performance of a proposed method is a crucial and necessary task to carry out in a research. This paper is focused on the statistical analysis of the results in the field of genetics-based machine Learning. It presents a study involving a set of techniques which can be used for doing a rigorous comparison among algorithms, in terms of obtaining successful classification models. Two accuracy measures for multiclass problems have been employed: classification rate and Cohen's kappa. Furthermore, two interpretability measures have been employed: size of the rule set and number of antecedents. We have studied whether the samples of results obtained by genetics-based classifiers, using the performance measures cited above, check the necessary conditions for being analysed by means of parametrical tests. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which supports the use of non-parametric statistics in the experimental analysis. In addition, non-parametric tests can be satisfactorily employed for comparing generic classifiers over various data-sets considering any performance measure. According to these facts, we propose the use of the most powerful non-parametric statistical tests to carry out multiple comparisons. However, the statistical analysis conducted on interpretability must be carefully considered.

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Section: Case Studies Of the Normality Propertymentioning

confidence: 99%

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

et al. 2008

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“…In this Section, we compare IG-RMiner with 10 other significant classification techniques, covering among others, evolutionary based techniques, ant colony optimization, fuzzy rule systems, and classic decision tree generators: ICRM [4], MPLCS [25], ILGA [26], CORE [27], SLAVE [28], GFS-GP [29], DTGA [30], AntMiner+ [31], RIPPER [32], C45R [33]. In particular, ICRM recently proved to be able to generate sufficiently accurate and easily interpretable rule classification systems.…”

Section: Comparison With Salient Classification Techniquesmentioning

confidence: 99%

Classification Rule Mining with Iterated Greedy

Pedraza

García-Martínez

Cano

et al. 2014

Lecture Notes in Computer Science

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Abstract. In the context of data mining, classification rule discovering is the task of designing accurate rule based systems that model the useful knowledge that differentiate some data classes from others, and is present in large data sets. Iterated greedy search is a powerful metaheuristic, successfully applied to different optimisation problems, which to our knowledge, has not previously been used for classification rule mining. In this work, we analyse the convenience of using iterated greedy algorithms for the design of rule classification systems. We present and study different alternatives and compare the results with state-of-the-art methodologies from the literature. The results show that iterated greedy search may generate accurate rule classification systems with acceptable interpretability levels.

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“…Building upon the above token competition idea, a coevolution-based classification method was proposed in [147] to coevolve individual rules and rule sets concurrently in separate coevolving populations in order to further confine the search space and produce quality rule sets efficiently.…”

Section: F Machine Learningmentioning

confidence: 99%

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

Epitropakis

Deb

et al. 2017

IEEE Trans. Evol. Computat.

251

101

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Abstract-Multi-modal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation is an important topic which has practical relevance in problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO prolems, if equipped with specifically-designed diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the more recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts on leveraging the capabilities of niching to facilitate various optimization (e.g., multi-objective and dynamic optimization) and machine learning (e.g., clustering, feature selection, and learning ensembles) tasks. A list of successful applications of niching methods to real-world problems is provided to demonstrate that niching methods are able to provide solutions that are difficult for conventional optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.

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A coevolutionary algorithm for rules discovery in data mining

Cited by 56 publications

References 44 publications

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

Classification Rule Mining with Iterated Greedy

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

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