Intelligent Database Systems Lab

Presenter: HONG, CHIA-TSE

Authors: Yen-Hsien Lee, Chih-Ping Wei, Tsang-Hsiang Cheng, Ching-Ting Yang

2012. DSS

Nearest-neighbor-based approach to time-series classification

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Outlines

Motivation Objectives Methodology Experiments Conclusions Comments

2

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Motivation• Prior classification analysis research predominately

focuses on constructing a classification model from

training instances that involve nontime-series

attributes.

• Traditional classification analysis techniques such

statistical-transformation-based approach often results

in information loss and, in turn, imperils classification

effectiveness.

(55, 45, 35, 25, 15) ( 5, 20, 35, 50, 65)

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Objectives• This study aims to propose and develop a novel time-

series classification technique based on the k-nearest-

neighbor (kNN) classification approach.

• The preservation of trends in time-series sequences

when inducing a classification model for a time-series

classification problem can reduce information loss.

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Methodology(review): Analysis and selection of learning strategy for time-series classification

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• Model-based learning strategy

• Instance-based learning strategy

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Methodology - kNN-based time-series classification technique

Decision combination methods

6

Time-series similarity measureKNN-TSC

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Experiments

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Experiments• Performance benchmark

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Experiments• Parameter tuning experiments

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Experiments• Comparative evaluation

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Conclusions

• The empirical results show that the proposed kNN-TSC

technique achieves better performance than the traditional

statistical-transformation-based approach does.

• With the use of the stratified average method for decision

combination, kNN-TSC technique can effectively handle the

asymmetric class-distribution problem.

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Comments

• Advantages- Achieves better performance.

• Applications- Time-series classification problems.