A new algorithm for automatic knowledge acquisition in inductive learning

Akgöbek, Ömer; Aydin, Yavuz Selim; Öztemel, Ercan; Aksoy, Mehmet Sabih

doi:10.1016/j.knosys.2006.03.001

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…The process of rules induction in REX-1 is as follows: REX-1 calculates basic entropy values and recalculated entropy values, then sets the attributes in ascending order to process the lowest entropy values first. The REX-1 algorithm is described below in figure 11 [29].…”

Section: Rule Extractor-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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Section: Rule Extractor-1mentioning

confidence: 99%

An Overview of Inductive Learning Algorithms

AlMana¹,

Aksoy²

2014

IJCA

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“…Textual knowledge conceptualisation can be processed through grammar structure analysis and the reasoning rule. The reasoning rule is applied to textual knowledge conceptualisation where knowledge extraction rules can be established manually or automatically (Akgo¨bek, Aydin, Ö ztemel, and Aksoy 2006;Gomez and Segami 2007). The reasoning rule is applied to textual knowledge conceptualisation where knowledge extraction rules can be established manually or automatically (Akgo¨bek, Aydin, Ö ztemel, and Aksoy 2006;Gomez and Segami 2007).…”

Section: Textual Knowledge Analysismentioning

confidence: 99%

A knowledge component extraction technology using figures and tables

Yang

Hou

Chen

2013

Journal of Experimental & Theoretical Artificial Intelligen

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With the growing complexity of document contents and the significant increase of domain knowledge, it is difficult for knowledge receivers to understand specific domain knowledge. However, traditional knowledge extraction schemes usually provide complete documents to knowledge receivers and much time is required for knowledge receivers to acquire domain knowledge. The concept of component-based knowledge is to divide documents into several knowledge components corresponding to more specific domains, which can be used to reduce the time required for the knowledge receivers to search the specific domain knowledge. Moreover, since the figures and tables in a document usually contain the important implicit knowledge expressed within the document, the aim of this research is to extract the knowledge components from documents (e.g. industry yearbooks) on the basis of figures and tables. In this research, a knowledge component extraction model with two algorithms, namely the keyword mapping algorithm and sentence mapping algorithm, is developed. In order to demonstrate the applicability of the proposed methodology, a web-based knowledge component extraction system is also established based on the proposed model. Furthermore, Taiwan Logistics Yearbooks are applied as examples to evaluate the proposed model. The verification results show that the developed system is a high-performance knowledge component extraction system. As a whole, this research provides an approach for knowledge receivers to efficiently and accurately acquire domain knowledge. domain knowledge and, in doing so, they spent much time reading irrelevant content (Hou and Lin 2004).If documents can be divided into several knowledge components, each corresponding to more specific domains that are underpinned by component-based knowledge, then the reader can efficiently search for specific domain knowledge. As the reader digests similar knowledge components, in order to acquire specific domain knowledge, he or she can search for domain-specific knowledge quickly and accurately by properly focusing on specific informational components.Critically implicit knowledge from documents is often given in the form of figures and tables, and domain-specific knowledge is embedded within the paragraphs surrounding these figures and tables. Notably, these paragraphs, which hold important information or knowledge, can be efficiently digested using component-based knowledge. If knowledge components can be extracted from documents based on figures and tables, then the time required for the reader to search for specific domain knowledge can be effectively reduced.Previously, when readers searched for specific domain knowledge through traditional knowledge extraction schemes, they had to digest a great deal of irrelevant content from entire documents in order to obtain the necessary domain-specific knowledge. In traditional knowledge extraction schemes, figures and tables served as an auxiliary tool which provided the intended contents for the reader. As such, the time require...

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“…Data mining covers basics like statistics, databases, programming techniques, and high performance processing, as well as all works for formulizing and applying induction procedures that would extract significant and useful information from available data [1]. Structural patterns and hidden knowledge in the large databases can be represented in various ways, such as decision tables, decision trees, association rules, and classification rules.…”

Section: Introductionmentioning

confidence: 99%

A Rule Induction Algorithm for Knowledge Discovery and Classification

Akgöbek¹

2013

Turk J Elec Eng & Comp Sci

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Classification and rule induction are key topics in the fields of decision making and knowledge discovery.The objective of this study is to present a new algorithm developed for automatic knowledge acquisition in data mining.The proposed algorithm has been named RES-2 (Rule Extraction System). It aims at eliminating the pitfalls and disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. In this study, 15 datasets consisting of multiclass values with different properties and sizes and obtained from the University of California, Irvine, have been used. Classification accuracy and rule count have been used to test the proposed method. This method presents an alternative 3-step method to classify categorical, binary, and continuous data by taking advantage of algorithms for data mining classification and decision rule generation. The method aims at improving the classification accuracy of the algorithms that extract the decision rules. Experimental studies were conducted on the benchmark datasets and the results of the comparisons with some known algorithms for decision rule generation have shown that the proposed method performs classification with a higher accuracy and generates fewer rules.

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A new algorithm for automatic knowledge acquisition in inductive learning

Cited by 17 publications

References 7 publications

An Overview of Inductive Learning Algorithms

An Overview of Inductive Learning Algorithms

A knowledge component extraction technology using figures and tables

A Rule Induction Algorithm for Knowledge Discovery and Classification

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