Frequent Subspace Classifier
Date Issued
2009
Date
2009
Author(s)
Cheng, Ching-Wei
Abstract
With the amount of the data increasing rapidly, it is infeasible to consider all the dimensions of the data to perform classification. Thus, constructing a classifier based on subspaces has attracted more and more attention. The previously proposed methods used randomly-generated or some subspaces to construct a classifier. Therefore, in this thesis, we propose a hybrid classification method, called FSC (Frequent subspace classifier), to generate all potential subspaces and utilize these subspaces to construct a classifier. Our proposed method consists of three phases. First, we apply the discrete wavelet transform to reduce the dimensions of feature vectors. Next, we employ the frequent subspaces mining method to derive all potential subspaces. Finally, we exploit AdaBoost to select the significant subspaces from the potential subspaces derived to construct an ensemble classifier. Since the FSC generates all potential subspaces and selects the subspaces based on the maximum entropy reduction, it provides more opportunities to construct an effective classifier. The experiment results show that the FSC outperforms the SVM and LogitBoost in both UCI and stock datasets.
Subjects
subspace
clustering
classifier
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