Relational Learning Using Constrained Confidence-Rated Boosting
نویسندگان
چکیده
In propositional learning, boosting has been a very popular technique for increasing the accuracy of classification learners. In firstorder learning, on the other hand, surprisingly little attention has been paid to boosting, perhaps due to the fact that simple forms of boosting lead to loss of comprehensibility and are too slow when used with standard ILP learners. In this paper, we show how both concerns can be addressed by using a recently proposed technique of constrained confidencerated boosting and a fast weak ILP learner. We give a detailed description of our algorithm and show on two standard benchmark problems that indeed such a weak learner can be boosted to perform comparably to state-of-the-art ILP systems while maintaining acceptable comprehensibility and obtaining short run-times.
منابع مشابه
Active relational rule learning in a constrained confidence rated boosting framework
In this dissertation, I investigate the potential of boosting within the framework of relational rule learning. Boosting is a particularly robust and powerful technique to enhance the prediction accuracy of systems that learn from examples. Although boosting has been extensively studied in the last years for propositional learning systems, only little attention has been paid to boosting in rela...
متن کاملEffective Rule Induction from Molecular Structures Represented by Labeled Graphs
Acyclic conjunctive queries form a polynomially evaluable fragment of definite nonrecursive first-order Horn clauses. Labeled graphs, a special class of relational structures, provide a natural way for representing chemical compounds. We propose an algorithm specific to learning acyclic conjunctive queries predicting certain properties of molecules represented by labeled graphs. To compensate f...
متن کاملImproving Algorithms for Boosting
Motivated by results in information-theory, we describe a modification of the popular boosting algorithm AdaBoost and assess its performance both theoretically and empirically. We provide theoretical and empirical evidence that the proposed boosting scheme will have lower training and testing error than the original (nonconfidence-rated) version of AdaBoost. Our modified boosting algorithm and ...
متن کاملAdaptive Incremental Learning for Statistical Relational Models Using Gradient-Based Boosting
We consider the problem of incrementally learning models from relational data. Most existing learning methods for statistical relational models use batch learning, which becomes computationally expensive and eventually infeasible for large datasets. The majority of the previous work in relational incremental learning assumes the model’s structure is given and only the model’s parameters needed ...
متن کاملLearning r-of-k Functions by Boosting
We investigate further improvement of boosting in the case that the target concept belongs to the class of r-of-k threshold Boolean functions, which answer “+1” if at least r of k relevant variables are positive, and answer “−1” otherwise. Given m examples of a r-of-k function and literals as base hypotheses, popular boosting algorithms (e.g., AdaBoost) construct a consistent final hypothesis b...
متن کامل