Characteristics of predictor sets found using differential prioritization

Background: Feature selection plays an undeniably important role in classification problems involving high dimensional datasets such as microarray datasets. For filter-based feature selection, two well-known criteria used in forming predictor sets are relevance and redundancy. However, there is a third criterion which is at least as important as the other two in affecting the efficacy of the resulting predictor sets. This criterion is the degree of differential prioritization (DDP), which varies the emphases on relevance and redundancy depending on the value of the DDP. Previous empirical works on publicly available microarray datasets have confirmed the effectiveness of the DDP in molecular classification. We now propose to establish the fundamental strengths and merits of the DDP-based feature selection technique. This is to be done through a simulation study which involves vigorous analyses of the characteristics of predictor sets found using different values of the DDP from toy da...

• Type: Journal
• Year: 2007
• Where: ALMOB
• Authors: Chia Huey Ooi, Madhu Chetty, Shyh Wei Teng
• Keywords: ALMOB 2007, Feature Selection, Microarray Datasets, Predictor Sets