NDLI: An Effective Integrated Method for Learning Big Imbalanced Data

Content Provider	IEEE Xplore Digital Library
Author	Ghanavati, M. Wong, R.K. Fang Chen Yang Wang Chang-Shing Perng
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia (Ghanavati, M.; Wong, R.K.) \|\| IBM Res., Yorktown Heights, NY, USA (Chang-Shing Perng) \|\| Nat. ICT Australia, Sydney, NSW, Australia (Fang Chen; Yang Wang)
Abstract	The imbalance of data has great effects on the performance of learning algorithms due to the presence of under-represented data and severe class distribution skews. This is one of the new challenges of machine learning data mining. Choosing a suitable metric that addresses the properties and domain characteristics of learning real-world data is critical for achieving a good result in most of machine learning and data mining algorithms. When the dataset is big and imbalanced, even with an accurate metric, it is extremely difficult to achieve good learning performance. This paper proposes an integrated method for learning large imbalanced datasets. In particular, a combination of metric learning algorithms and balancing techniques are experimented. Their performances are compared based on a set of evaluation metrics running on bootstrap datasets of different sizes. The best combination is then selected for learning the full imbalanced datasets. Experiments using the water pipeline datasets collected from various Australia regions in the past two decades show that our proposed method is both practical and effective.
Starting Page	691
Ending Page	698
File Size	506735
Page Count	8
File Format	PDF
e-ISBN	9781479950577
DOI	10.1109/BigData.Congress.2014.102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Learning systems Training Large Margin Nearest Neighbour Imbalanced data Error analysis Metric Learning Clustering algorithms Classification Mathematical model Equations
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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