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A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers

Mineichi KudoJack Sklansky — 1998

Kybernetika

Needs of feature selection in medium and large problems increases in many fields including medical and image processing fields. Previous comparative studies of feature selection algorithms are not satisfactory in problem size and in criterion function. In addition, no way has not shown to compare algorithms with different objectives. In this study, we propose a unified way to compare a large variety of algorithms. Our results show that the sequential floating algorithms promises for up to medium...

Piecewise linear classifiers preserving high local recognition rates

Hiroshi TenmotoMineichi KudoMasaru Shimbo — 1998

Kybernetika

We propose a new method to construct piecewise linear classifiers. This method constructs hyperplanes of a piecewise linear classifier so as to keep the correct recognition rate over a threshold for a training set. The threshold is determined automatically by the MDL (Minimum Description Length) criterion so as to avoid overfitting of the classifier to the training set. The proposed method showed better results in some experiments than a previous method.

Construction of nonlinear discrimination function based on the MDL criterion

Manabu SatoMineichi KudoJun ToyamaMasaru Shimbo — 1998

Kybernetika

Although a nonlinear discrimination function may be superior to linear or quadratic classifiers, it is difficult to construct such a function. In this paper, we propose a method to construct a nonlinear discrimination function using Legendre polynomials. The selection of an optimal set of Legendre polynomials is determined by the MDL (Minimum Description Length) criterion. Results using many real data show the effectiveness of this method.

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