IQ estimation for accurate time-series classification

Abstract

Due to its various applications, time-series classification is a prominent research topic in data mining and computational intelligence. The simple k-NN classifier using dynamic time warping (DTW) distance had been shown to be competitive to other state-of-the art time-series classifiers. In our research, however, we observed that a single fixed choice for the number of nearest neighbors k may lead to suboptimal performance. This is due to the complexity of time-series data, especially because the characteristic of the data may vary from region to region. Therefore, local adaptations of the classification algorithm is required. In order to address this problem in a principled way by, in this paper we introduce individual quality (IQ) estimation. This refers to estimating the expected classification accuracy for each time series and each k individually. Based on the IQ estimations we combine the classification results of several k-NN classifiers as final prediction. In our framework of IQ, we develop two time-series classification algorithms, IQ-MAX and IQ-WV. In our experiments on 35 commonly used benchmark data sets, we show that both IQ-MAX and IQ-WV outperform two baselines.