Hyperrelations in version space

A version space is a set of all hypotheses consistent with a given set of training examples, delimited by the specific boundary and the general boundary. In existing studies [4, 5, 3] a hypothesis is a conjunction of attribute-value pairs, which is shown to have limited expressive power [6].In this paper we investigate version space in a more expressive hypothesis space, where a hypothesis is a hyperrelation, which is in effect a disjunction of conjunctions of disjunctions of attribute-value pairs. We propose to use an inductive bias, E-set, which turns our attention to equilabelled, supported, and maximal hypertuples. We characterise version space in such a hypothesis space under this bias and show the relationship between the specific boundary and general boundary with respect to unequivocal data, a special subset of the data space. We present experimental results on some public datasets.

[1]  Hung Son Nguyen,et al.  From Optimal Hyperplanes to Optimal Decision Trees , 1998, Fundam. Informaticae.

[2]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[3]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[4]  David A. Bell,et al.  Data Reduction Based on Hyper Relations , 1998, KDD.

[5]  Andrzej Skowron,et al.  Discovery of Data Patterns with Applications to Decomposition and Classification Problems , 1998 .

[6]  Helmut Prendinger,et al.  Approximate Reasoning , 1997, EPIA.

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

[8]  Andrzej Skowron,et al.  Boolean Reasoning for Feature Extraction Problems , 1997, ISMIS.

[9]  David A. Bell,et al.  A Lattice Machine Approach to Automated Casebase Design: Marrying Lazy and Eager Learning , 1999, IJCAI.

[10]  Tom M. Mitchell,et al.  Version Spaces: A Candidate Elimination Approach to Rule Learning , 1977, IJCAI.

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

[12]  Haym Hirsh,et al.  Generalizing Version Spaces , 1994, Machine Learning.

[13]  Tom Michael Mitchell Version spaces: an approach to concept learning. , 1979 .

[14]  Ivo Düntsch,et al.  Classificatory filtering in decision systems , 2000, Int. J. Approx. Reason..

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

[16]  Andrzej Skowron,et al.  EXTRACTING LAWS FROM DECISION TABLES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[17]  Michèle Sebag,et al.  Delaying the Choice of Bias: A Disjunctive Version Space Approach , 1996, ICML.

[18]  G. Grätzer General Lattice Theory , 1978 .

[19]  Ivo Düntsch,et al.  Simple data filtering in rough set systems , 1998, Int. J. Approx. Reason..

[20]  Ivo Düntsch,et al.  Statistical evaluation of rough set dependency analysis , 1997, Int. J. Hum. Comput. Stud..

[21]  Sinh Hoa Nguyen,et al.  Pattern Extraction from Data , 1998, Fundam. Informaticae.