A Review of Rules Family of Algorithms

Aksoy, Mehmet Sabih

doi:10.3390/mca13010051

Cited by 12 publications

(14 citation statements)

References 20 publications

(20 reference statements)

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“…If no more unclassified examples, the procedure is finished. This continues until everything is classified properly or all iterations equal Na [19] [20]. This process can be seen in the flowchart below in figure 3 [19].…”

Section: Rules-1mentioning

confidence: 99%

“…The size of the array is equal to the total number of all values. At most, there can be Na iterations in the rule-forming procedure [19] [20].…”

Section: Rules-1mentioning

confidence: 99%

“…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%

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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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Section: Rules-1mentioning

confidence: 99%

“…The size of the array is equal to the total number of all values. At most, there can be Na iterations in the rule-forming procedure [19] [20].…”

Section: Rules-1mentioning

confidence: 99%

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An Overview of Inductive Learning Algorithms

AlMana¹,

Aksoy²

2014

IJCA

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“…This algorithm is designed to serve as an improved CA for application to features that take values on an infinite space. It is inspired by the RULES family 9 and the trialand-error interactions in RRL. It attempts to learn from scratch and build its experience to address numeric values in a manner similar to discrete ones.…”

Section: Rules-cont Algorithmmentioning

confidence: 99%

“…Such noise affects the accuracy of prediction models, which can cause agents to make poor decisions. This paper contributes to the field of CAs by introducing the use of relational reinforcement learning (RRL) in a CA family called RULES 9 . A novel nondiscretization approach using RRL is proposed, and based on this approach an algorithm called RULES with continuous attributes (RULES-CONT) is developed.…”

Section: Introductionmentioning

confidence: 99%

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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Categorization and Construction of Rule Based Systems

Liu

Gegov

Stahl

2014

Engineering Applications of Neural Networks

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Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‗if-then' rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.

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A Review of Rules Family of Algorithms

Cited by 12 publications

References 20 publications

An Overview of Inductive Learning Algorithms

An Overview of Inductive Learning Algorithms

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

Categorization and Construction of Rule Based Systems

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