PAC Learning with Irrelevant Attributes

We consider the problem of learning in the presence of irrelevant attributes in Valiant's PAC model (1984). In the PAC model, the goal of the learner is to produce an approximately correct hypothesis from random sample data. If the number of relevant attributes in the target function is small, it may be desirable to produce a hypothesis that also depends on only a small number of variables. Haussler (1988) previously considered the problem of learning monomials of a small number of variables. He showed that the greedy set cover approximation algorithm can be used as a polynomial-time Occam algorithm for learning monomials on r of n variables. A outputs a monomial on r(ln q+1) variables, where q is the number of negative examples in the sample. We extend this result by showing that there is a polynomial-time Occam algorithm for learning k-term DNF formulas depending on r of n variables that outputs a DNF formula depending on O(r/sup k/log/sup k/q) variables, where q is the number of negative examples in the sample. We also give a polynomial-time Occam algorithm for learning decision lists (sometimes called 1-decision lists) with k alternations. >

Authors

• Aditi Dhagat
• Lisa Hellerstein

Keywords

• Diseases
• Polynomials
• Testing
• Approximation algorithms
• Medical diagnosis
• Probably Approximately Correct Learning
• Learning Algorithms
• Presence Of Variants
• Functional Class
• Finite Set
• Set Of Covariates
• Algorithm For Problem
• Polynomial-time Algorithm
• Examples Of Functions
• Symmetric Function
• Monomial
• Boolean Function
• Samples For Example
• Target Concept
• Concept Of Class
• Random Sample Of Data
• Presence Of Learning
• Set Cover Problem
• Estimation Algorithm
• Presence Of Function
• Correction Algorithm
• Input Samples