Penalty Learning for Optimal Partitioning using Multilayer Perceptron
This work addresses the need for better penalty prediction in changepoint detection, which is incremental as it applies a standard MLP to a known bottleneck in an existing algorithm.
The study tackled the problem of predicting the optimal penalty for changepoint detection in sequences, using a multilayer perceptron (MLP) to improve accuracy and F1 score compared to existing linear or tree-based models on genomic datasets.
Changepoint detection is a technique used to identify significant shifts in sequences and is widely used in fields such as finance, genomics, and medicine. To identify the changepoints, dynamic programming (DP) algorithms, particularly Optimal Partitioning (OP) family, are widely used. To control the changepoints count, these algorithms use a fixed penalty to penalize the changepoints presence. To predict the optimal value of that penalty, existing methods used simple models such as linear or tree-based, which may limit predictive performance. To address this issue, this study proposes using a multilayer perceptron (MLP) with a ReLU activation function to predict the penalty. The proposed model generates continuous predictions -- as opposed to the stepwise ones in tree-based models -- and handles non-linearity better than linear models. Experiments on large benchmark genomic datasets demonstrate that the proposed model improves accuracy and F1 score compared to existing models.