Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction

RULES-5: A rule induction algorithm for classification problems involving continuous attributes

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/095440603322769929 ◽

2003 ◽

Vol 217 (12) ◽

pp. 1273-1286 ◽

Cited By ~ 22

Author(s):

D T Pham ◽

S Bigot ◽

S S Dimov

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Rule Extraction ◽

Rule Induction ◽

Divide And Conquer ◽

Benchmark Problems ◽

Machine Learning Algorithm ◽

Classification Problems ◽

Rule Sets

This paper presents RULES-5, a new induction algorithm for effectively handling problems involving continuous attributes. RULES-5 is a ‘covering’ algorithm that extracts IF-THEN rules from examples presented to it. The paper first reviews existing methods of rule extraction and dealing with continuous attributes. It then describes the techniques adopted for RULES-5 and gives a step-by-step example to illustrate their operation. The paper finally gives the results of applying RULES-5 and other algorithms to benchmark problems. These clearly show that RULES-5 generates rule sets that are more accurate than those produced by its immediate predecessor RULES-3 Plus and by a well-known commercially available divide-and-conquer machine learning algorithm.

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A Hyper-Heuristic for Descriptive Rule Induction

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch200 ◽

2008 ◽

pp. 3164-3175

Author(s):

Tho Hoan Pham ◽

Tu Bao Ho

Keyword(s):

Microarray Data ◽

Rule Induction ◽

Exhaustive Search ◽

Relative Accuracy ◽

Divide And Conquer ◽

The Third ◽

Search Heuristics ◽

Linear Rule ◽

Non Linear ◽

Covering Algorithms

There are in general three approaches to rule induction: exhaustive search, divide-and conquer, and separate-and-conquer (or its extension as weighted covering). Among them, the third approach, according to different rule search heuristics, can avoid the problem of producing many redundant rules (limitation of the first approach) or non-overlapping rules (limitation of the second approach). In this chapter, we propose a hyper-heuristic to construct rule search heuristics for weighted covering algorithms that allows producing rules of desired generality. The hyper-heuristic is based on a PN space, a new ROC-like tool for analysis, evaluation, and visualization of rules. Well-known rule search heuristics such as entropy, Laplacian, weight relative accuracy, and others are equivalent to ones proposed by the hyper-heuristic. Moreover, it can present new non-linear rule search heuristics, some are especially appropriate for description tasks. The non-linear rule search heuristics have been experimentally compared with others on the generality of rules induced from UCI datasets and used to learn regulatory rules from microarray data.

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