Combining Static and Dynamic Features for Multivariate Sequence Classification
This work addresses a practical issue in machine learning for real-world scenarios where mixed data types are common, though it is incremental as it builds on existing techniques.
The paper tackles the problem of classifying multivariate sequences that contain both static and dynamic features, proposing a hybrid approach that combines generative models like HMM and LSTM to extract temporal information and integrate it with static features, resulting in improved classification performance on several public datasets.
Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data. In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data. We explore how the extracted information can be combined with the static features in order to improve the classification performance. We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.