Marie-France Sagot, Maria Emilia M.T. Walter's Advances in Bioinformatics and Computational Biology: Second PDF

By Marie-France Sagot, Maria Emilia M.T. Walter

ISBN-10: 3540737308

ISBN-13: 9783540737308

This booklet constitutes the refereed lawsuits of the second one Brazilian Symposium on Bioinformatics, BSB 2007, held in Angra dos Reis, Brazil, in August 2007; co-located with IWGD 2007, the foreign Workshop on Genomic Databases.

The thirteen revised complete papers and six revised prolonged abstracts have been conscientiously reviewed and chosen from 60 submissions. The papers handle a wide diversity of present issues in computationl biology and bioinformatics that includes unique study in machine technology, arithmetic and records in addition to in molecular biology, biochemistry, genetics, medication, microbiology and different existence sciences.

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Additional resources for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31,

Sample text

As our crossover operator only produces combinations of existing partitions and no mutation is used, the search space will not be explored in details. Thus, the large amount of similar partitions will not be produced by MOCLE, favoring the concision of the set of solutions obtained. Finally, the objective functions should measure the quality of partitions in different ways, each one related to a diļ¬€erent clustering criterion. They should also Multi-Objective Clustering Ensemble with Prior Knowledge 39 complement each other.

Thus, the default value was used for the parameter strong, and the value 0 was used for the parameters noise and label (to have all points assigned to a cluster and no point excluded as noise). From the partitions created with these parameters values, we chose only those partitions having k in the interval of interest to be used in the initial population. In the experiments, we compare two versions of MOCLE: one unsupervised (MUH) and one that take prior knowledge into account (MSH). The only difference between them is that MSH includes the third objective function that depends on previous knowledge.

6], we aim at generating partitions with clusters as pure as possible regarding the class distribution. When used as criteria to the division of nodes in decision trees, the Gini index [6] and the information gain produce very similar trees [18]. However, the Gini index gives preference to divisions that place the largest class in one pure node and all other classes in another node [15]. Information gain, in its turn, favors the generation of nodes with balanced sizes. The application of the Gini index to our problem would favor partitions with a large pure cluster (considering the known structure) and a cluster that mixed the other classes, in detriment of partitions with subdivisions of the large class together with a good separation of the smaller classes, that would be preferred.

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Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, by Marie-France Sagot, Maria Emilia M.T. Walter


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