Skip to content
Supplementary material for the article "Combining Static and Dynamic Features for Multivariate Sequence Classification"
Python R Shell
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
ALL
Analysis
DataNexus
DetectDynamics
Documentation/Figures
HMM
LSTM
ModelsEleven
OnlyDynamic
Plots
RF-HMM
RF-LSTM
RF
.gitignore
LICENSE
README.md
notes.md

README.md

Generative Models in Classification

Supplementary material for the article "Combining Static and Dynamic Features for Multivariate Sequence Classification" presented at the DSAA 2016.

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.

You can’t perform that action at this time.