Download Advances in Bioinformatics and Computational Biology: Second by K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de PDF

By K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)

ISBN-10: 3540737308

ISBN-13: 9783540737308

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Read or Download Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings PDF

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Extra info for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings

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To minimize the occurrence of suboptimal solutions, we run KM 30 times for each value of k, each time with a random choice of initial centers. The partition with the lowest squared error for each value of k was chosen to take part of initial population. The AL and SL partitions were obtained by generating the trees and cutting them in order to produce one partition for each value of k. 8 and 1). In preliminary experiments, we noticed that varying the other parameters did not produce very different results.

Once this is done the corresponding score is computed. Table 1 compares the average solution quality (total score) of both algorithms for all instances. The first column indicates the specific instance name, the second column gives the average objective function values for the PbGA, the third column the average objective function for the MbGA, and columns third and fourth their respective standard deviations. Table 2 compares both average computation times and their respective standard deviations.

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. Therefore, we decided to use the information gain. 4 Experiments In order to evaluate our approach, we choose datasets that contain more than E one possible structure. , π En } is the set of known structures E for a given dataset, where n is the number of known structures and π Ej is the jth known structure.

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