Agent based data classification approach for data mining

Bakar, Azuraliza Abu; Othman, Zulaiha Ali; Hamdan, Abdul Razak; Yusof, Rizana; Ismail, Roslina

doi:10.1109/itsim.2008.4631677

Cited by 5 publications

(2 citation statements)

References 10 publications

(9 reference statements)

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“…The proposed agents are embedded within the standard rule application techniques. The result shows the significant improvements in classification time and the number of matched rules with comparable classification accuracy [37].…”

Section: Related Workmentioning

confidence: 88%

Students Performance Prediction System Using Multi Agent Data Mining Technique

AL-Ghamdi¹,

Malibari²,

Alkhozae³

2014

IJDKP

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A high prediction accuracy of the students' performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making. Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for the binary classification and not applicable to multiclass classification directly. SAMME boosting technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binary classification. In this paper, students' performance prediction system using Multi Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide help to the low students by optimization rules. The proposed system has been implemented and evaluated by investigate the prediction accuracy of Adaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed C4.5 single classifier and LogitBoost.

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Section: Related Workmentioning

confidence: 88%

Students Performance Prediction System Using Multi Agent Data Mining Technique

AL-Ghamdi¹,

Malibari²,

Alkhozae³

2014

IJDKP

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“…An agent can act as an information collector, preprocessor (Othman et al, 2007) and classifier (Bakar et al, 2008) to a user.…”

Section: Jcsmentioning

confidence: 99%

An Agent Based Framework for Sentiment Classification of Online Reviews Using Ontology

Kalaivani¹,

Shunmuganathan²

2014

Journal of Computer Science

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In this study, we design and develop an agent based framework for sentiment classification of online reviews using ontology. The book review ranking is based on the sentiment classification result. We propose a novel approach with the help of the JADE platform to solve problems by non-visual automatic sentiment classification. The description of book reviews ranking are generated from the ontology based mapping. This approach employs the data extraction agent which is used to retrieve the books comments i.e., the user reviews from the specified blogs. The Second agent is the recommendation agent i.e., domain ontology is used for identifying domain related features in comments. The Third agent is feature selection agent in which XML document content is split into single sentence. Each word in the sentence is mapped with ontology. A Mapping process is used for identifying the domain related sentences in that context. These processes are used for ranking the book results based on customer reviews. The book review ranking system can be extended to other product-review easily.

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