A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy

Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification ac ...

[1]  Padhraic Smyth,et al.  An Information Theoretic Approach to Rule Induction from Databases , 1992, IEEE Trans. Knowl. Data Eng..

[2]  Se June Hong.  R-MINI: An Iterative Approach for Generating Minimal Rules from Examples , 1997, IEEE Trans. Knowl. Data Eng..

[3]  Nick Cercone,et al.  Rule-Induction and Case-Based Reasoning: Hybrid Architectures Appear Advantageous , 1999, IEEE Trans. Knowl. Data Eng..

[4]  Marco Muselli,et al.  Binary Rule Generation via Hamming Clustering , 2002, IEEE Trans. Knowl. Data Eng..

[5]  Vira Chankong,et al.  Rough set-based approach to rule generation and rule induction , 2002, Int. J. Gen. Syst..

[6]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[7]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[8]  Dongsong Zhang,et al.  Discovering golden nuggets: data mining in financial application , 2004, IEEE Trans. Syst. Man Cybern. Part C.

[9]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[10]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[11]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[12]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[13]  Shaocheng Tong,et al.  Rough Set Research on Rule Extraction in Information Table , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[14]  Aboul Ella Hassanien,et al.  Fuzzy rough sets hybrid scheme for breast cancer detection , 2007, Image Vis. Comput..

[15]  Dayou Liu,et al.  Rough set based approach for inducing decision trees , 2007, Knowl. Based Syst..

[16]  Qinghua Hu,et al.  A weighted rough set based method developed for class imbalance learning , 2008, Inf. Sci..

[17]  Jie Zhou,et al.  Research of reduct features in the variable precision rough set model , 2009, Neurocomputing.

[18]  R. Mike Cameron-Jones,et al.  Induction of logic programs: FOIL and related systems , 1995, New Generation Computing.

[19]  Basilis Boutsinas,et al.  A method for improving the accuracy of data mining classification algorithms , 2009, Comput. Oper. Res..

[20]  Igor Skrjanc,et al.  New results in modelling derived from Bayesian filtering , 2010, Knowl. Based Syst..

[21]  Arnaud Doucet,et al.  A Bayesian approach to joint tracking and identification of geometric shapes in video sequences , 2010, Image Vis. Comput..

[22]  Liu Xi,et al.  Overview of Nonlinear Bayesian Filtering Algorithm , 2011 .

[23]  Ahmet Arslan,et al.  A Boolean function approach to feature selection in consistent decision information systems , 2011, Expert Syst. Appl..

[24]  Giampaolo E. D'Errico,et al.  À la Kalman Filtering for Metrology Tool With Application to Coordinate Measuring Machines , 2012, IEEE Transactions on Industrial Electronics.