NDLI: Performance-Optimizing Classification of Time-Series Based on Nearest Neighbor Density Approximation

Content Provider	IEEE Xplore Digital Library
Author	Ando, S.
Copyright Year	2012
Abstract	In time series classification, the nearest neighbor (NN) method is known to compare well against others over a wide range of benchmark data. However, adapting its instance-based learning format for application-specific goals, such as optimizing alternate performance measures or cost-sensitive learning is not a straightforward problem. In this paper, we attempt to extend the effectiveness of the NN model to address the practical cost settings and performance goals using a simple representation which takes the approximate density of each class as the numerical features of the data. As the nearest neighbor model exploits such approximations to predict the class with largest posterior probability, one can define a linear discriminative function of this feature which performs identically to the 1-NNclassifier. Further, the weight parameters of the function which intuitively represents the exponential weight of examples in the density estimation, can be trained to minimize the misclassification cost or to optimize an alternative performance measure such as F1. We evaluate the proposed representation inmulti-class classification and skewed class distribution problems using various public benchmarks. The results show that our approach outperforms the nearest neighbor and the SVMclassifiers based on the original time series features.
Starting Page	759
Ending Page	764
File Size	244915
Page Count	6
File Format	PDF
ISBN	9781467351645
e-ISBN	9781467351645
DOI	10.1109/ICDMW.2012.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Time series analysis Time-series Classification Weighted k-nearest neighbor Classification Benchmark testing Data models Trajectory Approximation methods
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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