Combining Static and Dynamic Features for Multivariate Sequence Classification

TL;DR

This paper proposes a hybrid approach combining static and dynamic features using generative models like HMM and LSTM to enhance multivariate sequence classification performance, outperforming existing methods on public datasets.

Contribution

It introduces a novel hybrid method that integrates static and dynamic features through generative models for improved classification accuracy.

Findings

Hybrid approach outperforms existing techniques on multiple datasets

Combining static and dynamic features improves classification accuracy

Using HMM and LSTM effectively extracts temporal information

Abstract

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.