# Ensemble Methods for Sequence Classification with Hidden Markov Models

arXiv: https://arxiv.org/abs/2409.07619 · HTML: https://mlsanity.com/paper/2409.07619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [Simplifying Neural Network Training Under Class Imbalance](https://mlsanity.com/paper/2312.02517/) (2023)
- [Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise](https://mlsanity.com/paper/2312.07087/) (2023)
- [Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering](https://mlsanity.com/paper/2011.01826/) (2020)
- [Event-based dataset for the detection and classification of manufacturing assembly tasks](https://mlsanity.com/paper/2405.14626/) (2024)

## Built on by (most cited first, 1 of 1)

- [TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure](https://mlsanity.com/paper/2602.23784/) (2026)

## Abstract (arXiv)

We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced or smaller datasets due to their simplicity, interpretability, and efficiency. These models are particularly effective in domains such as finance and biology, where traditional methods struggle with high feature dimensionality and varied sequence lengths. Our ensemble-based scoring method enables the comparison of sequences of any length and improves performance on imbalanced datasets. This study focuses on the binary classification problem, particularly in scenarios with data imbalance, where the negative class is the majority (e.g., normal data) and the positive class is the minority (e.g., anomalous data), often with extreme distribution skews. We propose a novel training approach for HMM Ensembles that generalizes to multi-class problems and supports classification and anomaly detection. Our method fits class-specific groups of diverse models using random data subsets, and compares likelihoods across classes to produce composite scores, achieving high average precisions and AUCs. In addition, we compare our approach with neural network-based methods such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), highlighting the efficiency and robustness of HMMs in data-scarce environments. Motivated by real-world use cases, our method demonstrates robust performance across various benchmarks, offering a flexible framework for diverse applications.