Machine-learning techniques and their applications in manufacturing

Pham, Duc Truong; Afify, A. A.

doi:10.1243/095440505x32274

Cited by 135 publications

(92 citation statements)

References 122 publications

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“…In general, current IL algorithms have been divided into two types: Decision tree (DT) and Covering Algorithm (CA) [2]. Each type has its own purpose, strength, and weakness.…”

Section: Introductionmentioning

confidence: 99%

Inductive Learning for Continuous Classes and the Effect of RULES Family

ElGibreen¹,

Aksoy²

2015

IJIET

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Abstract-Due to the digital world explosion and its appearance in everyday life, predicting numeric actions became necessary in machine learning. However, due to growth of interest in understanding how problems can be solved, simple prediction algorithms are more helpful than the difficult statistical approaches. Covering algorithm can be used to accomplish difficult problems using simple rules or trees. One family called RULES was found to be very interesting with appealing properties. It is one of the most flexible and simplest families with high learning rate. Nevertheless, even though RULES is actively improving but it is surprisingly neglected, especially with numerical datasets. Thus, the purpose of this paper is to extend the literature and investigate the problems of continuous classes in RULES and other inductive learning families. A theoretical analysis is conducted to show the effect of numerical actions and how it is still an open research area. An empirical evaluation is also provided to prove how RULES family can be used as the base of further improvement. Accordingly, this paper can be used as a reference by researchers to know what research area is still not covered and need further refinement in inductive learning.

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“…In general, current IL algorithms have been divided into two types: Decision tree (DT) and Covering Algorithm (CA) [2]. Each type has its own purpose, strength, and weakness.…”

Section: Introductionmentioning

confidence: 99%

Inductive Learning for Continuous Classes and the Effect of RULES Family

ElGibreen¹,

Aksoy²

2015

IJIET

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“…Han and Kamber [10] classified data mining systems based on various criteria such as kind of database mined, kind of knowledge mined, kind of technique utilized, application area adapted. Pham and Afify [11] reviewed machine learning techniques in the manufacturing domain. They evaluated the several machine learning techniques and examined application areas in which they have been successfully deployed.…”

Section: Introductionmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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“…These methods have been applied to a wide variety of problems in engineering [1][2][3] because of their ability to discover patterns from data. The integration of these methods with conventional decision support systems can provide a means for significantly improving the quality of decision making.…”

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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Machine-learning techniques and their applications in manufacturing

Cited by 135 publications

References 122 publications

Inductive Learning for Continuous Classes and the Effect of RULES Family

Inductive Learning for Continuous Classes and the Effect of RULES Family

Data mining in manufacturing: a review based on the kind of knowledge

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

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