RULES: A simple rule extraction system

Pham, Duc Truong; Aksoy, Mehmet Sabih

doi:10.1016/s0957-4174(99)80008-6

Cited by 44 publications

(30 citation statements)

References 5 publications

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“…We will take this research further by incorporating RULES-3 developed by Pham and Aksoy in [17] and later improved by Mathkour in [18], in the Edu Gate System and develop a tool for management to make their insightful decisions. Moreover, we will also focus on data selection, and preparation phases when the system is tested for accuracy, validation, and verification.…”

Section: Discussionmentioning

confidence: 99%

Data Mining in Higher Education

AlHammadi¹,

Aksoy²

2013

PEN

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Section: Discussionmentioning

confidence: 99%

Data Mining in Higher Education

AlHammadi¹,

Aksoy²

2013

PEN

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“…RULES-1 (RULe Extraction System-1) also known as RULES algorithm was developed by Pham and Aksoy [19] [20]. An implementation of RULES-1 extracts rules for objects in similar sets of classes.…”

Section: Rules-1mentioning

confidence: 99%

An Overview of Inductive Learning Algorithms

AlMana¹,

Aksoy²

2014

IJCA

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Inductive learning enables the system to recognize patterns and regularities in previous knowledge or training data and extract the general rules from them. In literature there are proposed two main categories of inductive learning methods and techniques. Divide-and-Conquer algorithms also called decision Tree algorithms and Separate-and-Conquer algorithms known as covering algorithms. This paper first briefly describe the concept of decision trees followed by a review of the well known existing decision tree algorithms including description of ID3, C4.5 and CART algorithms. A well known example of covering algorithms is RULe Extraction System (RULES) family. An up to date overview of RULES algorithms, and Rule Extractor-1 algorithm, their solidity as well as shortage are explained and discussed. Finally few application domains of inductive learning are presented.

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“…The score of each new rule is computed and the rule with the best accuracy is remembered (step (4) in Figure 2). The new rule is then inserted into the NewPartialRules list (step (9) in Figure 2) unless one of the conditional tests (step (5), (6) or (7) in Figure 2) prevents this because it is deemed that no improved rule will be obtained from the new rule. In the latter case, the new rule is regarded as ineffective and additional specialisations will not improve the values for the quality measure.…”

Section: Rules-6mentioning

confidence: 99%

“…The specialisation process is then repeated until the PartialRules list becomes empty (step (2) in Figure 2) due to the tests at steps (5), (6), (7) and (10). It should be noted that the PartialRules and NewPartialRules lists are reused after each iteration.…”

Section: Rules-6mentioning

confidence: 99%

“…RULES (RULe Extraction System) is a family of inductive learning algorithms that follow the rule induction approach. The first three algorithms in the RULES family of algorithms (RULES-1, 2 and 3) were developed by Pham and Aksoy [5][6][7]. Later, the rule forming procedure of RULES-3 was improved by Pham and Dimov [8] and the new algorithm was called RULES-3 Plus.…”

Section: Introductionmentioning

confidence: 99%

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RULES-6: a simple rule induction algorithm for supporting decision making

Pham

Afify

2005

31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.

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-RULES-3 Plus is a member of the RULES family of simple inductive learning algorithms with successful engineering applications. However, it requires modification in order to be a practical tool for problems involving large data sets. In particular, efficient mechanisms for handling continuous attributes and noisy data are needed. This paper presents a new rule induction algorithm called RULES-6, derived from the RULES-3 Plus algorithm. The algorithm employs a fast and noise-tolerant search method for extracting IF-THEN rules from examples. It also uses simple and effective methods for rule evaluation and continuous attributes handling. A detailed empirical evaluation of the algorithm is reported in the paper. The results presented demonstrate the strong performance of the algorithm.

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RULES: A simple rule extraction system

Cited by 44 publications

References 5 publications

Data Mining in Higher Education

Data Mining in Higher Education

An Overview of Inductive Learning Algorithms

RULES-6: a simple rule induction algorithm for supporting decision making

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