Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data

Nascimento, André; Prudêncio, Ricardo B. C.; Souto, Michael Vandesteen Silva; Costa, Ivan G.

doi:10.1007/978-3-642-04277-5_3

Cited by 19 publications

(16 citation statements)

References 19 publications

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“…In recent years, Meta-Learning has been extrapolated to other domains of application, such as in the selection of time series forecasting models [18], design of planning systems [25], combinatorial optimization [26], software engineering [27] and bioinformatics [9,28,29]. In such domains, Meta-Learning can be seen as tool for analysis of experiments performed by using a number of algorithms on a large set of problems that can be solved by these algorithms.…”

Section: Meta-learningmentioning

confidence: 99%

Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach

Prudêncio

Souto

Ludermir

2011

Studies in Computational Intelligence

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Abstract. In this work, we present the use of Ranking Meta-Learning approaches to ranking and selecting algorithms for problems of time series forecasting and clustering of gene expression data. Given a problem (forecasting or clustering), the Meta-Learning approach provides a ranking of the candidate algorithms, according to the characteristics of the problem's dataset. The best ranked algorithm can be returned as the selected one. In order to evaluate the Ranking Meta-Learning proposal, prototypes were implemented to rank artificial neural networks models for forecasting financial and economic time series and to rank clustering algorithms in the context of cancer gene expression microarray datasets. The case studies regard experiments to measure the correlation between the suggested rankings of algorithms and the ideal rankings. The results revealed that Meta-Learning was able to suggest more adequate rankings in both domains of application considered.

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Section: Meta-learningmentioning

confidence: 99%

Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach

Prudêncio

Souto

Ludermir

2011

Studies in Computational Intelligence

Self Cite

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“…Even though this is a common approach for selection/ranking of supervised learning algorithms [20], in the area of clustering this is a relatively new and unexplored topic [21]. Still, preliminary results in the area of microarray data clustering [21], [22] are promising. These studies explicitly concluded that clustering meta-learning frameworks would benefit from including a larger number of algorithms.…”

Section: Discussionmentioning

confidence: 99%

A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

Vukičević

Delibašić

Jovanović

et al. 2012

2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Abstract-We propose a method for designing a partitioning clustering algorithm from reusable components that is suitable for finding the appropriate number of clusters (K) in microarray data. The proposed method is evaluated on 10 datasets (4 syntetic and 6 real-word microarrays) by considering 1008 reusable-componentbased algorithms and four normalization methods. The best performing algorithm were reported on every dataset and also rules were identified for designing microarray-specific clustering algorithms. The obtained results indicate that in the majority of cases a data-tailored clustering algorithm design outperforms the results reported in the literature. In addition, data normalization can have an important influence on algorithm performance. The method proposed in this paper gives insights for design of divisive clustering algorithms that can reveal the optimal K in a microarray dataset.

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“…In the last years, concepts and techniques of meta-learning have been applied in various application domains, such as selection of models of temporal series prediction [9], software engineering [16] and bioinformatics [17], [18], [19], [10]. In the literature, there are different interpretations (definition) of the term meta-learning, as in [8], [20], [21].…”

Section: Meta-learningmentioning

confidence: 99%

Characterization measures of ensemble systems using a meta-learning approach

Parente

Canuto

Xavier-Júnior

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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In a decision making process, we are usually oriented to take into consideration all the relevant features (characteristics) involved in a specific problem. In Machine Learning, for instance, a decision is made through the use of a learning algorithm and the characterization process is represented by the corresponding datasets. In this context, classification algorithms can be applied, individually or through the use of ensemble systems (combination of classification methods), in the decision-making process. The concept of ensemble systems has emerged in the last decades as a strategy for combining independent classifiers (components), aiming to provide a decision that is potentially more effective than any single component. However, the design of the ensemble structure is not an easy task and it can have an important impact in the performance of these systems. In this paper, we investigate the use of meta-learning on the selection of the best configuration parameters (learning strategy, size and individual classifiers) for homogeneous structures of ensemble systems. The main aim of this analysis is to assess the effect of using meta-learning in the design of efficient and robust ensemble systems.

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Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data

Cited by 19 publications

References 19 publications

Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach

Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach

A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

Characterization measures of ensemble systems using a meta-learning approach

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