NDLI: A hybrid coupled k-nearest neighbor algorithm on imbalance data

Content Provider	IEEE Xplore Digital Library
Author	Chunming Liu Longbing Cao Yu, P.S.
Copyright Year	2014
Description	Author affiliation: Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA (Yu, P.S.) \|\| Adv. Analytics Inst., Univ. of Sydney Technol., Sydney, NSW, Australia (Chunming Liu; Longbing Cao)
Abstract	The state-of-the-art classification algorithms rarely consider the relationship between the attributes in the data sets and assume the attributes are independently to each other (IID). However, in real-world data, these attributes are more or less interacted via explicit or implicit relationships. Although the classifiers for class-balanced data are relatively well developed, the classification of class-imbalanced data is not straightforward, especially for mixed type data which has both categorical and numerical features. Limited research has been conducted on the class-imbalanced data. Some algorithms mainly synthesize or remove instances to force the sizes of each class comparable, which may change the inherent data structure or introduces noise to the source data. While for the distance or similarity based algorithms, they ignored the relationship between features when computing the similarity. This paper proposes a hybrid coupled k-nearest neighbor classification algorithm (HC-kNN) for mixed type data, by doing discretization on numerical features to adapt the inter coupling similarity as we do on categorical features, then combing this coupled similarity to the original similarity or distance, to overcome the shortcoming of the previous algorithms. The experiment results demonstrate that our proposed algorithm can get a higher average performance than that of the relevant algorithms (e.g. the variants of kNN, Decision Tree, SMOTE and NaiveBayes).
Starting Page	2011
Ending Page	2018
File Size	420952
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889798
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Size measurement Couplings Classification algorithms Clouds Training data
Content Type	Text
Resource Type	Article

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