NDLI: A new design of multiple classifier system and its application to the classification of time series data

Content Provider	IEEE Xplore Digital Library
Author	Lei Chen Kamel, M.S.
Copyright Year	2007
Description	Author affiliation: Univ. of Waterloo, Waterloo (Lei Chen)
Abstract	In this paper, we propose the scheme of multiple input representation-adaptive ensemble generation and aggregation(MIR-AEGA) for the classification of time series data. MIR-AEGA employs a set of heterogeneous classifiers, each of which takes a different representation of time series data as the input. MIR-AEGA adopts an "overfitting and selection" strategy. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data ' globally in different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking consideration into both the ability of each ensemble to fit the validation data locally and the possible overfitting effects. We claim that MIR-AEGA has two advantages (1) By using multiple representations, it exploits the temporal information of time series data as much as possible, thus could improve the overall performance (2) By tweaking the trade-off between the ability to fit the validation data and the overfitting effects, we expect the performance of this method is reliable in different situations. In this paper, the performance of MIR- AEGA is also assessed experimentally in comparison with other benchmark techniques. The experimental results demonstrate the good performance and the reliability of MIR-AEGA for the classification of time series data.
Starting Page	385
Ending Page	391
File Size	169134
Page Count	7
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4413686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Testing Aggregates Finance Biometrics Chemistry Speech Application software Computer architecture Topology
Content Type	Text
Resource Type	Article

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