Hierarchical and Semi-supervised Multiclass Classification based on the Dimension Reduction Method
Date Issued
2015
Date
2015
Author(s)
Huang, Chiao-Ching
Abstract
In this thesis, we introduced the application of supervised dimension reduction method: kernel sliced inverse regression (KSIR) on the domain of multiclass classification and semi-supervised learning. Our method performs robustly in the problems with numerous classes and scared training data. Multiclass classification contains more than two classes and is usually complicated to understand the relation and difficult to classify. Common approaches train numerous classifiers applied on all observations according to their strategies and unidentified relations. We proposed a hierarchical classification method which decomposed the multiclass problem into a tree-structured problem set based on KSIR. The data on the effective dimension reduction (e.d.r) subspace constructed by KSIR can be depicted to reveal the relations between classes and decompose the problem, build the hierarchical tree, and reveal the class relations. Our approach provides a good strategy for multiclass classification and decreases the number of applied classifiers. It behaved with comparable accuracy in the experiment of public datasets, compared with the classic method. In our cases study of flight engine diagnosis, resulting in good performance and succeeded in classifying the most indistinguishable classes. The other case study of Scene Classification on public data: SUN presents that our hierarchical decomposition strategy performs robustly in the classifying numerous classes. Our second part is an attempt to draw on the research of applying our method into the semi-supervised domain. Many real world problems can be formulated as semi-supervised problems since the acquisition of labeled data often requires domain knowledge. We proposed a semi-supervised dimension reduction achieved with the KSIR by using prior information to estimate the statistical parameters in the KSIR formula. In the semi-supervised situations, our method provides not only a good strategy for classification but also a suitable subspace for conditioned label spreading. With our hierarchical strategy and label spreading, classification performs better accuracy in the semi-supervised problems.
Subjects
Dimension reduction
Hierarchical classification
semi-supervised classification
multiclass classification
classification strategy
Type
thesis
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