6533b85ffe1ef96bd12c1bcb
RESEARCH PRODUCT
Entropy-Based Classifier Enhancement to Handle Imbalanced Class Problem
Sergei ParshutinArnis KirshnersHenrihs Gorskissubject
Computer scienceEntropy (statistical thermodynamics)business.industryDecision treePattern recognition02 engineering and technologycomputer.software_genre01 natural sciencesSynthetic data010305 fluids & plasmasEntropy (classical thermodynamics)0103 physical sciences0202 electrical engineering electronic engineering information engineeringGeneral Earth and Planetary SciencesEntropy (information theory)020201 artificial intelligence & image processingArtificial intelligenceData miningEntropy (energy dispersal)businessEntropy (arrow of time)computerGeneral Environmental ScienceEntropy (order and disorder)description
The paper presents a possible enhancement of entropy-based classifiers to handle problems, caused by the class imbalance in the original dataset. The proposed method was tested on synthetic data in order to analyse its robustness in the controlled environment with different class proportions. As also the proposed method was tested on the real medical data with imbalanced classes and compared to the original classification algorithm results. The medical field was chosen for testing due to frequent situations with uneven class ratios.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 | Procedia Computer Science |