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

Pham, Duc Truong; Bigot, Samuel; Dimov, Stefan Simeonov

doi:10.1243/095440603322769929

Cited by 34 publications

(31 citation statements)

References 15 publications

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“…Although offline discretization reduces the time required for rule induction, it can severely affect the quality of the induced rules. 28 In particular, there is a considerable trade-off between the number of intervals used and the consistency of the rules. Choosing a small number of split points increases the interval size, which results in inconsistent rules; and choosing a large number of split points reduces the interval size which gives an overspecialized rules model.…”

Section: Offline Discretizationmentioning

confidence: 99%

“…However, CAQ does not obtain appropriate ranges because it operates only on the current example without considering the data as a whole. RULES-5 28 was designed to define the interval of each feature during rule construction based on the distribution of the examples. For each seed example, an interval is chosen such that the value of the most similar negative example is excluded.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

“…C and D are the lists of all numeric and discrete features of the data, respectively. V 28 , is a distance measure that can be used to compare any type of examples and all types of data.…”

Section: Raction Space: Missing/numeric Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Adopting Relational Reinforcement Learning in Covering Algorithms for Numeric and Noisy Environments

ElGibreen¹,

Aksoy²

2016

IJCIS

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Covering algorithms (CAs) constitute a type of inductive learning for the discovery of simple rules to predict future activities. Although this approach produces powerful models for datasets with discrete features, its applicability to problems involving noisy or numeric (continuous) features has been neglected. In real-life problems, numeric values are unavoidable, and noise is frequently produced as a result of human error or equipment limitations. Such noise affects the accuracy of prediction models and leads to poor decisions. Therefore, this paper studies the problem of CAs for data with numeric features and introduces a novel non-discretization algorithm called RULES-CONT. The proposed algorithm uses relational reinforcement learning (RRL) to resolve the current difficulties when addressing numeric and noisy data. The technical details of the algorithm are thoroughly explained to demonstrate that RULES-CONT contribute upon the RULES family by collecting its own knowledge and intelligently re-uses previous experience. The algorithm overcomes the infinite-space problem posed by numeric features and treats these features similarly to those with discrete values, while incrementally discovering the optimal rules for dynamic environments. It is the first RRL algorithm that intelligently induces rules to address continuous and noisy data without the need for discretization or pruning. To support our claims, RULES-CONT is compared with 7 well-known algorithms applied to 27 datasets with four levels of noise using 10-fold crossvalidation, and the results are analyzed using box plots and the Friedman test. The results show that the use of RRL results in significantly improved noise resistance compared with all other algorithms and reduces the computation time of the algorithm compared with the preceding version, which does not use relational representation.

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Section: Offline Discretizationmentioning

confidence: 99%

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

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Adopting Relational Reinforcement Learning in Covering Algorithms for Numeric and Noisy Environments

ElGibreen¹,

Aksoy²

2016

IJCIS

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“…Pham et al [24] invented RULES-5 in 2003 which built on the advantages of RULES-3 Plus . They tried to overcome some insufficiencies of RULES-3 Plus algorithm in their newly developed algorithm called RULES-5.…”

Section: Rules-5mentioning

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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“…When the STM is full, RULES-4 invokes RULES-3 Plus to generate rules. Pham et al [11] described another algorithm also based on RULES-3 Plus, called RULES-5, which can effectively handle problems involving continuous attributes. As with RULES-3 Plus, RULES-5 employs the H measure for evaluating the quality of rules.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 34 publications

References 15 publications

Adopting Relational Reinforcement Learning in Covering Algorithms for Numeric and Noisy Environments

Adopting Relational Reinforcement Learning in Covering Algorithms for Numeric and Noisy Environments

An Overview of Inductive Learning Algorithms

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

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