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Erschienen in:
2012 | OriginalPaper | Buchkapitel
The Decomposed K-Nearest Neighbor Algorithm for Imbalanced Text Classification
verfasst von : Hyung-Seok Kang, Kihyo Nam, Seong-in Kim
Erschienen in: Future Generation Information Technology
Verlag: Springer Berlin Heidelberg
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As textual data have exponentially increased, it is focused that a need for automatic classification of relevant data to one of pre-defined classes. In many practical applications, they assume that training data are evenly distributed among all classes, but they are suffered from an imbalanced problem. Several algorithms and re-sampling methods have been proposed to overcome an imbalanced problem, but they are still facing the overfitting and information missing. This paper proposes the Decomposed K-Nearest Neighbor (DCM-KNN). In training step, the DCM-KNN decomposes training data into misclassified and correctly-classified data set based on the result of traditional KNN, and finds the appropriate KNN for each set. In test step, the DCM-KNN estimates whether test data is similar to misclassified and correctly-classified data set, and applies the appropriate KNNs. Experimental results show that proposed algorithm can achieve more accurate results in an imbalanced condition.