Titlebook: Intelligent Data Engineering and Automated Learning - IDEAL 2009; 10th International C Emilio Corchado,Hujun Yin Conference proceedings 200

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脊椎动物

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boisterous

SCIS: Combining Instance Selection Methods to Increase Their Effectiveness over a Wide Range of Domahis area, but none of them consistently outperforms the others over a wide range of domains. In this paper we present a set of measures to characterize the databases, as well as a new algorithm that uses these measures and, depending on the data characteristics, it applies the method or combination

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谷类

冒失

Nearest Neighbor Classification by Relearningver, improving performance of the classifier is still attractive to cope with the high accuracy processing. A tolerant rough set is considered as a basis of the classification of data. The data classification is realized by applying the kNN with distance function. To improve the classification accur

BYRE

Integrating Rough Set and Genetic Algorithm for Negative Rule Extraction ¬. →¬.. By integrating rough set theory and genetic algorithm, we propose a coverage matrix based on rough set to interpret the solution space and then transform the negative rule extraction into set cover problem which can be solved by genetic algorithm. We also develop a rule extraction system ba

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休战

Lazy Classification Using an Optimized Instance-Based Learnercurrently available for performing classification, among which decision trees and artificial neural networks. In this article we describe the implementation of a new lazy classification model called similarity classifier. Given an out-of-sample instance, this model predicts its class by finding the