Literature DB >> 21216397

A new dataset evaluation method based on category overlap.

Sejong Oh1.   

Abstract

The quality of dataset has a profound effect on classification accuracy, and there is a clear need for some method to evaluate this quality. In this paper, we propose a new dataset evaluation method using the R-value measure. This proposed method is based on the ratio of overlapping areas among categories in a dataset. A high R-value for a dataset indicates that the dataset contains wide overlapping areas among its categories, and classification accuracy on the dataset may become low. We can use the R-value measure to understand the characteristics of a dataset, the feature selection process, and the proper design of new classifiers.
Copyright © 2010 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2011        PMID: 21216397     DOI: 10.1016/j.compbiomed.2010.12.006

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  3 in total

1.  Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance.

Authors:  Guang-Hui Fu; Jia-Bao Wang; Min-Jie Zong; Lun-Zhao Yi
Journal:  Metabolites       Date:  2021-06-14

2.  CBFS: high performance feature selection algorithm based on feature clearness.

Authors:  Minseok Seo; Sejong Oh
Journal:  PLoS One       Date:  2012-07-06       Impact factor: 3.240

3.  A machine learning approach for specification of spinal cord injuries using fractional anisotropy values obtained from diffusion tensor images.

Authors:  Bunheang Tay; Jung Keun Hyun; Sejong Oh
Journal:  Comput Math Methods Med       Date:  2014-01-21       Impact factor: 2.238

  3 in total

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