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k-NN Ensemble DARA Approach to Learning Relational

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Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 520))

Abstract

Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years. Many approaches have been developed in order to learn relational data. One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA). The DARA algorithm is designed to summarize relational data with one-to-many relations. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset. A feature selection process can be introduced to overcome this problem. These selected features can then be further optimized to achieve a good classification result when using k Nearest Neighbour (k-NN) classifier. Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier. The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. Based on the results obtained, it shows that the proposed k-NN ensemble is able to improve the performance of traditional k-NN classifiers.

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Acknowledgements

This work has been supported by the Research Grant Scheme project funded by the Ministry of Education (MOE), Malaysia, under Grants No. RAG0007-TK-2012 and FRGS/2/2014/ICT02/UMS/02/1.

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Authors and Affiliations

  1. Knowledge Technology Research Unit, Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400, Kota Kinabalu, Sabah, Malaysia

    Rayner Alfred, Kung Ke Shin, Kim On Chin, HuiKeng Lau & M. H. A. Hijazi

Authors
  1. Rayner Alfred
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  2. Kung Ke Shin
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  3. Kim On Chin
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  4. HuiKeng Lau
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  5. M. H. A. Hijazi
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Corresponding author

Correspondence to Rayner Alfred .

Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia

    Mohamed Othman

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  4. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Hairulnizam Mahdin

  6. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

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Alfred, R., Shin, K.K., Chin, K.O., Lau, H., Hijazi, M.H.A. (2019). k-NN Ensemble DARA Approach to Learning Relational. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_22

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