An efficient multicategory classifier based on AdaBoosting

• Main Authors: Hong II Kim, Sang Hwa Lee, Nam Ik Cho
• Format: Conference Proceeding
• Language: English
• Subjects: Boosting; Computer science; Data mining; Error analysis; Feature extraction; Machine learning; Machine learning algorithms; Neural networks; Pattern recognition
• DOI: 10.1109/ICMLA.2005.9

In this paper, we propose an efficient multicategory classifier based on AdaBoosting scheme. The multicategory problems can be solved by multiple use of two-category classifiers or by use of a single classifier with multiple discriminant functions. In the case of boosting algorithms, since the use of simple classifier is one of the most important ingredients, they have focused on two-category classifier for each weak classifier. But for applying the two-category booster to m-category problems, we need O(m/sup 2/) boosters instead of O(m) ones arrangement scheme of the boosters as like detector-pyramid (S.Z. Li and Z. Zhang, 2004). We propose a multicategory boosting algorithm named M-Booster, where each weak classifier is the multicategory classifier. We focused on efficient method to extract the features and update the weights of data. The label for the each category is represented by m-dimensional vector, and the weights for the feature and other parameters are also modified accordingly. We have performed simulation for the artificial data and the face data with different rotation angles. It is shown that the use of single M-Booster can solve the multicategory problems more efficiently than the method based on 2-category classifiers and previous method (Adaboost.MH) (Y. Freund and R.E. Schapire, 1997).