6533b7d7fe1ef96bd1268d12

RESEARCH PRODUCT

Irrelevant Features, Class Separability, and Complexity of Classification Problems

Iryna Skrypnyk

subject

Computational complexity theoryCovariance matrixComputer sciencebusiness.industryFeature extractionPattern recognitionArtificial intelligencebusinessMachine learningcomputer.software_genreClass (biology)computerClass separability

description

In this paper, analysis of class separability measures is performed in attempt to relate their descriptive abilities to geometrical properties of classification problems in presence of irrelevant features. The study is performed on synthetic and benchmark data with known irrelevant features and other characteristics of interest, such as class boundaries, shapes, margins between classes, and density. The results have shown that some measures are individually informative, while others are less reliable and only can provide complimentary information. Classification problem complexity measurements on selected data sets are made to gain additional insights on the obtained results.

yearjournalcountryeditionlanguage
2011-11-012011 IEEE 23rd International Conference on Tools with Artificial Intelligence
https://doi.org/10.1109/ictai.2011.171