Yamaguchi University

Takao Tamesa

Masaaki Oka

Yoshihiko Hamamoto

Norio Iizuka

Horiyuki Ogihara

An information processor can logically support prediction based on past statistical information even though the information contains qualitative or non-numerical data. The processor determines whether an input pattern corresponding to an input object (a determination target) belongs to a specific class among multiple classes, based on feature subsets of any combination of a plurality of features, each feature comprises multiple categories. The processor includes a storage storing the input pattern corresponding to the input object and samples corresponding to respective sample objects and a classification determiner determining whether the input pattern belongs to the specific class. The classification determiner calculates a first conditional probability and a second conditional probability based on the number of the samples belonging to each category of the respective features, the first conditional probability is a probability that the data of the input pattern belong to categories corresponding to the respective feature for the specific class, the second conditional probability is a probability that the data of the input pattern belong to categories corresponding to the respective features for a non-specific class which is a class other than the specific class among classes, and the number of the samples is counted for each class based on the feature information on the samples and the class label information on the samples, and determines whether the input pattern belongs to the specific class based on the feature information on the input pattern, the first conditional probability and the second conditional probability.

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Yamaguchi, Japan

Takao Tamesa