Paper Info
Title
A Study on the Importance of Differential Prioritization in Feature Selection Using Toy Datasets
Abstract
Previous empirical works have shown the effectiveness of differential prioritization in feature selection prior to molecular classification. We now propose to determine the theoretical basis for the concept of differential prioritization through mathematical analyses of the characteristics of predictor sets found using different values of the DDP (degree of differential prioritization) from realistic toy datasets. Mathematical analyses based on analytical measures such as distance between classes are implemented on these predictor sets. We demonstrate that the optimal value of the DDP is capable of forming a predictor set which consists of classes of features which are well separated and are highly correlated to the target classes --- a characteristic of a truly optimal predictor set. From these analyses, the necessity of adjusting the DDP based on the dataset of interest is confirmed in a mathematical manner, indicating that the DDP-based feature selection technique is superior to both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods. Applying similar analyses to real-life multiclass microarray datasets, we obtain further proof of the theoretical significance of the DDP for practical applications.
Year
DOI
Venue
2008
10.1007/978-3-540-88436-1_27
PRIB
Keywords
Field
DocType
microarray datasets,feature selection,ddp-based feature selection technique,optimal value,mathematical analysis,toy datasets,differential prioritization,simplistic rank-based selection,mathematical manner,predictor set,optimal predictor set
Data mining,Feature selection,Computer science,Prioritization,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
5265
0302-9743
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Chia Huey Ooi1584.25
Shyh Wei Teng215121.02
Madhu Chetty336939.17