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