Intelligent Database Systems Lab Presenter: HONG, CHIA-TSE Authors: Yen-Hsien Lee, Chih-Ping Wei, Tsang-Hsiang Cheng, Ching-Ting Yang DSS Nearest-neighbor-based approach to time- series classification

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments 1

Intelligent Database Systems Lab 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)

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab Methodology(review): Analysis and selection of learning strategy for time-series classification 4 Model-based learning strategy Instance-based learning strategy

Intelligent Database Systems Lab Methodology - kNN-based time-series classification technique Decision combination methods 5 Time-series similarity measure KNN-TSC

Intelligent Database Systems Lab 6 Experiments

Intelligent Database Systems Lab 7 Experiments Performance benchmark

Intelligent Database Systems Lab 8 Experiments Parameter tuning experiments

Intelligent Database Systems Lab 9 Experiments Comparative evaluation

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab Comments Advantages - Achieves better performance. Applications - Time-series classification problems.