Name
Abstract | ||
|---|---|---|
Many interesting applications involve predictions based on a time-series sequence or a set of time-series sequences, which are referred to as time-series classification problems. Prior classification analysis research predominately focuses on constructing a classification model from training instances that involve non-time-series attributes. Direct application of traditional classification analysis techniques to time-series classification problems requires the transformation of time-series attributes into non-time-series ones by applying some statistical operations (e.g., average, sum, variance). However, such statistical-transformation-based approach often results in information loss and, in turn, imperils classification effectiveness. In this study, we propose a time-series classification technique based on the k-nearest-neighbor (kNN) classification approach. Using churn prediction of the mobile telecommunications industry as an evaluation application, our empirical evaluation results show that the proposed kNN-based time-series classification (kNN-TSC) technique achieves better performance (measured by miss and false alarm rates) than the statistical-transformation-based approach does. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.dss.2011.12.014 | Decision Support Systems |
Keywords | Field | DocType |
proposed knn-based time-series classification,time-series classification problem,statistical-transformation-based approach,classification model,time-series classification technique,prior classification analysis research,traditional classification analysis technique,imperils classification effectiveness,nearest-neighbor-based approach,time-series sequence,classification approach,data mining | k-nearest neighbors algorithm,Data mining,Data set,False alarm,Classification rule,One-class classification,Computer science,Artificial intelligence,Linear classifier,Mobile telephony,Machine learning,Multiclass classification | Journal |
Volume | Issue | ISSN |
53 | 1 | 0167-9236 |
Citations | PageRank | References |
13 | 0.56 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yen-hsien Lee | 1 | 118 | 16.64 |
| Chih-ping Wei | 2 | 743 | 74.20 |
| Tsang-Hsiang Cheng | 3 | 141 | 12.02 |
| Ching-Ting Yang | 4 | 13 | 0.56 |