Handling the description noise using an attribute value ontology

[1]  Makoto Haraguchi,et al.  Data abstractions for decision tree induction , 2003, Theor. Comput. Sci..

[2]  Hussein Almuallim,et al.  An Efficient Algorithm for Finding Optimal Gain-Ratio Multiple-Split Tests on Hierarchical Attributes in Decision Tree Learning , 1996, AAAI/IAAI, Vol. 1.

[3]  David Haussler,et al.  Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework , 1988, Artif. Intell..

[4]  Vasant Honavar,et al.  Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data , 2006, Knowledge and Information Systems.

[5]  Hideki Tanaka,et al.  Decision Tree Learning Algorithm with Structured Attributes: Application to Verbal Case Frame Acquisition , 1996, COLING.

[6]  Adrian Walker,et al.  On Retrieval from a Small Version of a Large Data Base , 1980, VLDB.

[7]  Marlon Núñez,et al.  The Use of Background Knowledge in Decision Tree Induction , 1991, Machine Learning.

[8]  Ray J. Hickey,et al.  Noise Modelling and Evaluating Learning from Examples , 1996, Artif. Intell..

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

[10]  Peter Clark,et al.  Induction in Noisy Domains , 1987, EWSL.

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

[12]  Vasant Honavar,et al.  Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction , 2002, SARA.

[13]  Xindong Wu.  Knowledge Acquisition from Databases , 1995 .

[14]  James A. Hendler,et al.  Ontology-based Induction of High Level Classification Rules , 1997, DMKD.

[15]  Xingquan Zhu,et al.  Class Noise vs. Attribute Noise: A Quantitative Study , 2003, Artificial Intelligence Review.

[16]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.