Optimizing shapelets quality measure for imbalanced time series classification

Qiuyan Yan

doi:10.1007/s10489-019-01535-z

Optimizing shapelets quality measure for imbalanced time series classification

2019-07-30

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DOI：10.1007/s10489-019-01535-z

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Time series classification has been considered as one of the most challenging problems in data mining and is widely used in a broad range of fields. A biased distribution leads to classification on minority time series objects more severe. A commonly taken approach is to extract or select the representative features to retain the structure of a time series object. However, when the data distribution is imbalanced, the traditional features cannot represent time series effectively, especially in multi-class environment. In this paper, Shapelets - a primitive time series mining technology - is applied to extract the most representative subsequences. Especially, we verify that IG (Information Gain) is unsuitable as a shapelet quality measure for imbalanced data sets. Nevertheless, we propose two quality measures for shapelets on imbalanced binary and multi-class problem respectively. Based on extracted shapelet features, we select the diversified top-k shapelets based on new quality measure to represent the top-k best features and achieve this procedure on map-reduce framework. Lastly, two oversampling methods based on shapelet features are proposed to re-balance the binary and multi-class time series data sets. We validated our methods on the benchmark data sets by comparing with the canonical classifiers and the state-of-the-art time series algorithms. It is verified that the proposed algorithms perform more competitive than the compared methods in statistical significance.

Keywords:

Imbalanced time series data

Feature selection

Shapelets

Quality measure

Optimizing shapelets quality measure for imbalanced time series classification

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