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Investigating the Class-Specific Relevance of Predictor Sets Obtained from DDP-Based Feature Selection Technique

  • Chia Huey Ooi
  • Madhu Chetty
  • Shyh Wei Teng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)

Abstract

Feature selection is crucial to tumor classification due to the high dimensionality of microarray datasets. With the aid of the degree of differential prioritization (DDP) between relevance and antiredundancy, our proposed DDP-based feature selection technique is capable of achieving better accuracies than those reported in previous studies, while using fewer genes in the predictor set. Additionally, we discovered a strong correlation between the DDP parameter in our feature selection technique and the number of classes in the dataset. This leads us to question if the measure of relevance in our feature selection technique becomes less efficient at capturing the class-specific relevance for each individual class of the dataset as the number of classes increases. In this study, we analyze the class-specific relevance of the predictor sets found using our feature selection technique. The analysis ultimately lays down the theoretical foundation for a beneficial improvement to our feature selection technique.

Keywords

Feature Selection Benchmark Dataset Microarray Dataset Class Accuracy Tumor Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chia Huey Ooi
    • 1
  • Madhu Chetty
    • 1
  • Shyh Wei Teng
    • 1
  1. 1.Gippsland School of Information TechnologyMonash UniversityChurchillAustralia

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