No abstract

Multivariate time series data classification has recently attracted interests from both industry and academia, as sensors used in various industries produce a lot of multivariate time series data. Having a lot of features, feature selection from those time series is essential to efficiently construct a classifier. In this paper, we propose a feature selection method to efficiently select features from the multivariate time series data considering variation. The candidate feature set is too large to efficiently select features and there are some feature redundancies. The proposed method can efficiently resolve these problems, and is validated by real datasets obtained from UCI Machine Learning Repository. Experiments show that the proposed method outperforms the typical feature selection methods in terms of accuracy and precision.

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model. †