Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

Martínez-Ballesteros, M.; Troncoso, Alicia; Martínez‐Álvarez, Francisco; Riquelme, José C.

doi:10.3233/ica-2010-0340

Cited by 51 publications

(33 citation statements)

References 37 publications

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“…Thus, when Δb > 0.011 the LM 4 model is provided (see equation (10). In this model, the most significative coefficient is that corresponding to Δt with a weight of -0.3696, revealing that the longer is the time elapsed, the smaller is the magnitude of the current earthquake.…”

Section: M5p Resultsmentioning

confidence: 99%

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Computational Intelligence Techniques for Predicting Earthquakes

Martínez‐Álvarez

Troncoso

Morales‐Esteban

et al. 2011

Lecture Notes in Computer Science

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Abstract. Nowadays, much effort is being devoted to develop techniques that forecast natural disasters in order to take precautionary measures. In this paper, the extraction of quantitative association rules and regression techniques are used to discover patterns which model the behavior of seismic temporal data to help in earthquakes prediction. Thus, a simple method based on the k-smallest and k-greatest values is introduced for mining rules that attempt at explaining the conditions under which an earthquake may happen. On the other hand patterns are discovered by using a tree-based piecewise linear model. Results from seismic temporal data provided by the Spanish's Geographical Institute are presented and discussed, showing a remarkable performance and the significance of the obtained results.

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Section: M5p Resultsmentioning

confidence: 99%

“…To fulfill this goal, the authors avoided the specification of the actual minimum support, which is the main contribution of this work. Finally, an extension of the well-known binary-coded CHC algorithm is presented in [10] for finding existing relations between atmospheric pollution and climatological conditions.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence Techniques for Predicting Earthquakes

Martínez‐Álvarez

Troncoso

Morales‐Esteban

et al. 2011

Lecture Notes in Computer Science

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“…The main motivation of this paper is to extend preliminary works such as the proposed algorithms called QARGA [51,52] and EQAR [53], to a multi-objective approach based on the NSGA-II algorithm able to deal with biological problems. In particular, a non-dominated multi-objective evolutionary algorithm is proposed in this work which is able to find QAR in databases with continuous attributes avoiding the discretization step.…”

Section: Mining Association Rules: a Reviewmentioning

confidence: 99%

“…The proposed algorithm extends the main features of QARGA [51,52] and EQAR [53] adding new features to improve the AR mining task. The most important improvement achieved in GarNet is related with solving the main drawbacks caused by the weighted objective scheme existing in the fitness function.…”

Section: Evolutionary Process Of Garnetmentioning

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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“…In preliminary works such as the proposed algorithms in [14] and [15], henceforth called QARGA (Quantitative Association Rules by Genetic Algorithm), authors of this paper developed several single-objective EA that use a weighting scheme for the fitness function which involved some evaluation measures. However, it is known that a scheme of this nature is not ideal compared to multiobjective schemes, so that could reduce the features used in the fitness function for applying a multi-objective technique.…”

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confidence: 99%