Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

Luna, José María; Romero, José Raúl; Ventura, Sebastián

doi:10.1007/s10115-011-0419-z

Cited by 67 publications

(40 citation statements)

References 36 publications

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“…We intend to explore the following avenues of research: 1) to include some interpretability criteria in the search process of IG-RMiner in order to obtain even simpler rule classification systems without a drastic undesirable effect on their accuracy levels; and 2) to analyse the application of IG adaptations to other data mining problems, such as rule association mining [35].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Classification Rule Mining with Iterated Greedy

Pedraza

García-Martínez

Cano

et al. 2014

Lecture Notes in Computer Science

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“…6.15). The crossover and mutation genetic operators are the same used by G3PARM and described in [19], which are based on the fact that highly frequent items might produce frequent patterns and, therefore, frequent association rules. The main idea of these genetic operators is to replace the least frequent item defined within a parent to produce a new more frequent item.…”

Section: Genetic Programmingmentioning

confidence: 99%

“…As it was demonstrated by many authors, the use of evolutionary algorithms for mining patterns of interest plays an important role [19,31]. This issue, together with the necessity of optimizing not only a single quality measure but also more than one, has given rise to the use of MOEAs in pattern mining [6].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Approaches in Pattern Mining

Ventura

Luna

2016

Pattern Mining With Evolutionary Algorithms

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In pattern mining, solutions tend to be evaluated according to several conflicting quality measures and, sometimes, these measures have to be optimized simultaneously. The problem of optimizing more than one objective function is known as multiobjective optimization, which together with evolutionary computation have given rise to evolutionary multiobjective optimization. This issue is well addressed in this chapter, which describes the usefulness of evolutionary multiobjective optimization in the pattern mining field. First, some fundamental concepts of evolutionary multiobjective optimization are introduced, including metrics to evaluate the quality of the solutions, and measures to be combined for the pattern mining problem. Then, different multiobjective approaches in the pattern mining field are described, which are divided into genetic algorithms, genetic programming approaches and other kind of algorithms like evolutionary computation and swarm intelligence. Finally, some useful real-world applications are analysed.

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“…EA are search algorithms that generate solutions for optimization problems using techniques inspired by natural evolution. Evolutionary computation is usually used to discover AR in both EA [42,43] and Genetic Programming [44,45] due to they offer a set of advantages for knowledge extraction and specifically for rule induction processes [46].…”

Section: Mining Association Rules: a Reviewmentioning

confidence: 99%

Discovering gene association networks by multi-objective evolutionary quantitative association rules

Martínez-Ballesteros

Nepomuceno-Chamorro

Riquelme

2014

Journal of Computer and System Sciences

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In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. 1.IntroductionSince late 1990s, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. Microarray technology has revolutionized the biological research because it allows to study thousand of genes or even whole genomes [1].As molecular biology is rapidly evolving into a quantitative science, it increasingly relies on computational algorithms to make sense of high-throughput data. One of the main goals in Microarray analysis is the reconstruction of gene regulatory processes and a key task is the inference of regulatory interactions among genes from gene expression data [2].Our aimisto infer the relationships between genes from an organism in a particular biological process. This relationships can be modeled in several levels of abstraction, these levels range from the detailed gene regulatory processes (where a chain of intracellular reaction activates a regulatory molecule, transcription factors until a protein is synthesized) to the high models of abstraction named gene association networks. In the reconstruction of gene regulatory processes, building gene association networks has been proven to provide useful insights for such task, the reconstruction of gene regulatory processes. A gene association network can be defined as a graph in which nodes represent genes and edges represent the influence between them. Our goal in this work is the inference of gene association networks from Microarray datasets.There are several statistical methods to infer gene association networks from Microarray data. A microarray dataset is a bidimensional data structure where conditions are experiments or sources and the columns are gene expression values. In our problem the conditions will be the instances and the gene expression values will be the attributes or features. These methods range from relatively straightforward correlation-based methods to more sophisticated methods based on

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Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

Cited by 67 publications

References 36 publications

Classification Rule Mining with Iterated Greedy

Classification Rule Mining with Iterated Greedy

Multiobjective Approaches in Pattern Mining

Discovering gene association networks by multi-objective evolutionary quantitative association rules

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