Constructing Nominal X-of-N Attributes

Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called X-of-N, that constructs new nominal attributes in the form of X-of-N representations An X-of-N is a Bet containing one or more attribute-value pairs. For a given instance, its value corresponds to the number of its attribute-value pairs that are true. The promising preliminary experimental results, on both artificial and real-world domains, show that constructing new nominal attributes in the form of X-of-N representations can significantly improve the performance of selective induction in terms of both higher prediction accuracy and lower theory complexity.

[1]  Ryszard S. Michalski,et al.  Pattern Recognition as Knowledge-Guided Computer Induction , 1978 .

[2]  Martin Stacey,et al.  Scientific Discovery: Computational Explorations of the Creative Processes , 1988 .

[3]  Ting Kai Ming,et al.  An m-of-n rule induction algorithm and its application to DNA domain , 1994, 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences.

[4]  Zijian Zheng,et al.  Continuous-valued X-of-n Attributes versus Nominal X-of-n Attributes for Constructive Induction: a Case Study , 1995 .

[5]  Thierry Van de Merckt Decision Trees in Numerical Attribute Spaces , 1993, IJCAI.

[6]  P. Utgoff,et al.  Multivariate Versus Univariate Decision Trees , 1992 .

[7]  R. Michalski,et al.  Multistrategy Constructive Induction: AQ17-MCI , 1993 .

[8]  M. Pazzani,et al.  ID2-of-3: Constructive Induction of M-of-N Concepts for Discriminators in Decision Trees , 1991 .

[9]  Jason Catlett,et al.  On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.

[10]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[11]  Larry A. Rendell,et al.  Lookahead Feature Construction for Learning Hard Concepts , 1993, International Conference on Machine Learning.

[12]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[13]  Sebastian Thrun,et al.  The MONK''s Problems-A Performance Comparison of Different Learning Algorithms, CMU-CS-91-197, Sch , 1991 .

[14]  G. Pagallo ADAPTATIVE DECISION TREE ALGORITHMS FOR LEARNING FROM EXAMPLES (Ph.D. Thesis) , 1990 .

[15]  Ronald L. Rivest,et al.  Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[16]  Herbert A. Simon,et al.  Scientific discovery: compulalional explorations of the creative process , 1987 .

[17]  Kent A. Spackman,et al.  Learning Categorical Decision Criteria in Biomedical Domains , 1988, ML.

[18]  Kai Ming Ting An M-of-N Rule Induction Algorithm and its Application to DNA Domain , 1994, HICSS.

[19]  Larry A. Rendell,et al.  Constructive Induction On Decision Trees , 1989, IJCAI.

[20]  Thomas G. Dietterich,et al.  A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping , 1990, ML.