The fitness-rough: A new attribute reduction method based on statistical and rough set theory

摘要

Abstract. Attribute reduction has become an important pre-processing task to reduce the complexity of the data miningntask. Rough reducts, statistical methods and correlation-based methods have gradually contributed towards improving attributenreduction techniques to a certain extent. Statistical methods are generally lower in computational complexity compared to thenrough reducts and the correlation-based methods, but many have proven that the rough reducts method is significant in reducingnimportant attributes without causing too much information loss. Correlation-based methods on the other hand evaluate featuresnas a subset instead of individual attribute. In this paper, we propose a combination of statistical and rough set methods to reducenimportant attributes in a simpler way while maintaining a lesser degree of information loss from the raw data. The fitness-roughnmethod (FsR) indicates important attributes from raw data and it is further simplified to a more compact information table.nBesides that, we have also looked into the problem of information loss in this method. Ten UCI machine learning datasetsnwere used as testing sets on the proposed method as compared to the classical rough reducts (RR) method, the statisticalnentropy (ENT) method and the correlation-based feature selection (CFS) method. Experimental results show that our methodnhas performed comparatively well with higher reduction strength and smaller rules set against the benchmarking methods,nespecially in medium size datasets. However, the FsR method is basically less efficient when used on mix-mode and nominalndatasets as the non-quantitative attributes involved in these datasets are normally pre-categorised.