Induction of decision trees

  title={Induction of decision trees},
  author={J. Ross Quinlan},
  journal={Machine Learning},
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic… 
Automated Induction of Rule-based Neural Networks from Databases
This paper describes the approach to the problem of automated knowledge acquisition from large databases of examples using an information-theoretic approach that enables a prototype expert system to be automatically generated and up and running in a matter of minutes, compared with months using a manual knowledge-acquisition approach.
Finding Cost-Efficient Decision Trees
This thesis extends the methods used in past research to look for decision trees with a smaller expected-cost than those found using a simple heuristic, and finds that exact approaches in general do not find lower expected- cost decision trees than heuristic approaches.
Towards Automatic Domain Knowledge Extraction for Evolutionary Heuristics
A framework in which a form of automatic domain knowledge extraction can be implemented using concepts from the field of machine learning is introduced and the result is an encoding of the type used in most evolutionary computation (EC) algorithms.
Learning Optimal Decision Trees with SAT
This paper develops a SAT-based model for computing smallest-size decision trees given training data and the proposed SAT model is shown to scale for publicly available datasets of practical interest.
A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces
The aim is to test the feasibility of different inductive learning techniques to perform the automatic acquisition of linguistic knowledge within a natural language database interface by comparing the results achieved by different instance-based and model-based learning algorithms.
Parallel genetic programming for decision tree induction
  • G. Folino, C. Pizzuti, G. Spezzano
  • Computer Science
    Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001
  • 2001
A parallel genetic programming approach to induce decision trees in large data sets is presented and preliminary experiments on data sets from the UCI machine learning repository give good classification outcomes and assess the scalability of the method.
Improving induction decision trees with parallel genetic programming
The method is able to deal with large data sets since it uses a parallel implementation of genetic programming through the grid model and performance results show a nearly linear speedup.
Rough-Set based Criteria for Incremental Rule Induction
A new framework for incremental learning based on accuracy and coverage is proposed, which classies a set of formulae into three layers: rule layer, subrule layer and non-rule layer by using the inequalities obtained.