Rule induction

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search
Decision Tree

Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data.

Data mining in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures.[1]:415– In the easiest case, a rule is expressed with “if-then statements” and was created with the ID3 algorithm for decision tree learning.[2]:7[1]:348 Rule learning algorithm are taking training data as input and creating rules by partitioning the table with cluster analysis.[2]:7 A possible alternative over the ID3 algorithm is genetic programming which evolves a program until it fits to the data.[3]:2

Creating different algorithm and testing them with input data can be realized in the WEKA software.[3]:125 Additional tools are machine learning libraries for Python like scikit-learn.

Paradigms[edit]

Some major rule induction paradigms are:

Algorithms[edit]

Some rule induction algorithms are:

References[edit]

  1. ^ a b Evangelos Triantaphyllou; Giovanni Felici (10 September 2006). Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Springer Science & Business Media. ISBN 978-0-387-34296-2.
  2. ^ a b Alex A. Freitas (11 November 2013). Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer Science & Business Media. ISBN 978-3-662-04923-5.
  3. ^ a b Gisele L. Pappa; Alex Freitas (27 October 2009). Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach. Springer Science & Business Media. ISBN 978-3-642-02541-9.
  4. ^ Sahami, Mehran. "Learning classification rules using lattices." Machine learning: ECML-95 (1995): 343-346.