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    Instance Selection and Feature Weighting Using Evolutionary Algorithms

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    Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real world applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a genetic algorithm (GA) and evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier

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    Description

    Title : Instance Selection and Feature Weighting Using Evolutionary Algorithms
    Author(s) : Jose-federico Ramirez-Cruz, Olac Fuentes, Vicente Alarcon-Aquino, Luciano Garcia-Banuelos
    Abstract : Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real world applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a genetic algorithm (GA) and evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2006

    Affiliations Universidad de las Americas Puebla
    Journal : 2006 15th International Conference on Computing
    Publisher : IEEE
    Pages : 73 - 79
    Url : http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4023791
    Isbn : 0-7695-2708-6
    Doi : 10.1109/CIC.2006.42

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