Rule Combination in Inductive Learning
نویسنده
چکیده
This paper describes the work on methods for combining rules obtained by machine learning systems. Three methods for obtaining the classification of examples with those rules are compared. The advantages and disadvantages of each method are discussed and the results obtained on three real world domains are commented. The methods compared are: selection of the best rule; PROSPECTOR-like probabilistic approximation for rule combination; and MYCIN-like approximation. Results show significant differences between methods indicating that the problem-solving strategy is important for accuracy oflearning systems.
منابع مشابه
Multimodal Inductive Reasoning: Combining Rule-Based and Case-Based Learning
Pedro Domingos Sec. Sistemas, Dept. Eng. Mecfiafica Instituto Superior T6cnico Av. Rovisco Pais Lisbon 1096, Portugal pedrod~gia.ist.utl.pt http://www.gia.ist.utl.pt/~pedrod Multimodal inductive reasoning is the combination of multiple learning paradigms in a single system. This article describes RISE, a combination of rule induction and case-based (or instance-based) learning, and uses experim...
متن کاملThe BBG Rule Induction Algorithm
We present an algorithm (BBG) for inductive learning from examples that outputs a rule list. BBG uses a combination of greedy and branch-and-bound techniques, and naturally handles noisy or stochastic learning situations. We also present the results of an empirical study comparing BBG with Quinlan's C4.5 on 1050 synthetic data sets. We nd that BBG greatly outperforms C4.5 on rule-oriented probl...
متن کاملComparative Analysis of Machine Learning Algorithms with Optimization Purposes
The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches. Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data. In this paper, a methodology has been employed to opt...
متن کاملThe Design and Evaluation of a Rule Induction Algorithm
We present an algorithm for inductive learning from examples that outputs an ordered list of if-then rules as its hypothesis. The algorithm uses a combination of greedy and branch-and-bound techniques, and naturally handles noisy or stochastic learning situations. We also present the results of an empirical study comparing our algorithm with Quinlan's C4.5 on 1050 synthetic data sets. We nd tha...
متن کاملThe role of feature construction in inductive rule learning
This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of these subtasks may have its own declarative bias, search strategies, and heuristics. In particular, we ...
متن کاملInductive Learning for Rule Generation from Ontology
This paper presents an idea of inductive learning use for rule generation from ontologies. The main purpose of the paper is to evaluate the possibility of inductive learning use in rule generation from ontologies and to develop the way how this can be done. Generated rules are necessary to supplement or even to develop the Semantic Web Expert System (SWES) knowledge base. The SWES emerges as th...
متن کامل