Feature Selection Based on Iterative Orthogonal Experimental Design
Date Issued
2007
Date
2007
Author(s)
Yen, Shih-Chieh
DOI
en-US
Abstract
Feature selection is an important issue in the problem of machine learning. Especially in the domain of activity recognition, many researchers try to make use of multiple heterogeneous sensors and thus receive a large amount of signals. Many features can be extracted, hence feature selection becomes more important. In this thesis, we propose a feature selection method based on orthogonal experimental design and compare this method with equential forward feature selection in terms of the accuracy of the model induced by the selected feature subset versus the number of treatments and the number of selected features.
Subjects
機器學習
特徵選擇
正交實驗
Feature Selection
Orthogonal Experimental Design
Type
thesis
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