Using conjunction of attribute values for classification

Advances in the efficient discovery of frequent itemsets have led to the development of a number of schemes that use frequent itemsets to aid developing accurate and efficient classifiers. These approaches use the frequent itemsets to generate a set of composite features that expand the dimensionality of the underlying dataset. In this paper, we build upon this work and (i) present a variety of schemes for composite feature selection that achieve a substantial reduction in the number of features without adversely affecting the accuracy gains, and (ii) show (both analytically and experimentally) that the composite features can lead to improved classification models even in the context of support vector machines, in which the dimensionality can automatically be expanded by the use of appropriate kernel functions.

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

[2]  George Karypis,et al.  LPMiner: an algorithm for finding frequent itemsets using length-decreasing support constraint , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[3]  PeiJian,et al.  Mining Frequent Patterns without Candidate Generation , 2000 .

[4]  James Parker,et al.  on Knowledge and Data Engineering, , 1990 .

[5]  Mohammed J. Zaki.  Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[6]  Mohammed J. Zaki.  Generating non-redundant association rules , 2000, KDD '00.

[7]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[8]  Zijian Zhang,et al.  Constructing Conjunctive Attributes using Production Rules , 2000, J. Res. Pract. Inf. Technol..

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

[10]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

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

[12]  Mohammed J. Zaki,et al.  Predicting Failures in Event Sequences , 2001 .

[13]  Mohammed J. Zaki,et al.  Mining features for sequence classification , 1999, KDD '99.

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

[15]  Ramesh C Agarwal,et al.  Depth first generation of long patterns , 2000, KDD '00.

[16]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[17]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

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

[19]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[20]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[22]  Mohammed J. Zaki,et al.  PlanMine: Predicting Plan Failures Using Sequence Mining , 1998, Artificial Intelligence Review.

[23]  Zijian Zheng,et al.  A Comparison of Constructive Induction with Diierent Types of New Attribute , 1996 .

[24]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[25]  J. Meigs,et al.  WHO Technical Report , 1954, The Yale Journal of Biology and Medicine.

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

[27]  Sayan Mukherjee,et al.  Feature Selection for SVMs , 2000, NIPS.

[28]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[29]  Nello Cristianini,et al.  Advances in Kernel Methods - Support Vector Learning , 1999 .

[30]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[31]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .