Rule Induction Algorithm

A rule induction algorithm is an Induction Algorithm that produces a Rule-based Model (to solve an Rule Induction Task).

AKA: Rule-based Induction Algorithm, Rule-based Algorithm, Rule-based Model Induction Algorithm, Rule-based Method, Rule-based Approach.
Context:
- It can range from being a Propositional Rule Induction Algorithm to being a First-Order Rule Logic Rule Induction Algorithm.
Example(s):
Counter-Example(s):
- a Regression Algorithm
See: Decision Tree Induction Algorithm, Inductive Logic Programming, Propositional Rule, Firs-Order Logic Rule.

(Palmer, 1997) ⇒ David D. Palmer. (1997). "A Trainable Rule-based Algorithm for Word Segmentation." In: Proceedings of the ACL 1997 Conference. doi:10.3115/976909.979658.
- This paper presents a trainable rule-based algorithm for performing word segmentation....

(Domingos, 1996) ⇒ Pedro Domingos. (1996). "Unifying Instance-based and Rule-based Induction." In: Machine Learning, 24(2). doi:10.1023/A:1018006431188
- Rule induction algorithms (Michalski, 1983; Michalski, Mozetic, Hong & Lavrac, 1986; Clark & Niblett, 1989; Rivest, 1987) typically employ a set covering or “separate and conquer” approach to induction.

(Cohen, 1995) ⇒ William W. Cohen. (1995). "Fast Effective Rule Induction." In: Proceedings of the Twelfth International Conference on Machine Learning (ICML 1995).
- ABSTRACT: Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error rates higher than those of C4.5 and C4.5rules. We then propose a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5rules with respect to error rates, but much more efficient on large samples. RIPPERk obtains error rates lower than or equivalent to C4.5rules on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.

(Brill, 1993a) ⇒ Eric David Brill. (1993). "A Simple Rule-based Part of Speech Tagger." In: Proceedings of the third Conference on Applied Natural Language Processing. doi:10.3115/974499.974526

(Muggleton, 1991) ⇒ Stephen Muggleton. (1991). "Inductive Logic Programming." In: Journal of New Generation Computing, 8(4). doi:10.1007/BF03037089

(Clark & Nibblet, 1989) ⇒ P. Clark, and T. Niblett. (1989). "The CN2 Induction Algorithm." In: Machine Learning, 3.

(Rivest, 1987) ⇒ R. L. Rivest. (1987). "Learning Decision Lists." In: Machine Learning, 2.

R. S. Michalski, I. Mozetic, J. Hong, and N. Lavrac. (1986). "The multi-purpose incremental learning system AQ 15 and its testing application to three medical domains." In: Proceedings of the Fifth National Conference on Artificial Intelligence.

R. S. Michalski. (1983). "A Theory and Methodology of Inductive Learning." In: Artificial Intelligence, 20.