Active partitioning: inverting the paradigm of active learning

TL;DR

This paper introduces active partitioning, a novel algorithm that uses model competition to identify and separate functional patterns in datasets, leading to improved model specialization and performance.

Contribution

It presents a new partitioning method that inverts active learning by reinforcing model strengths, enabling better dataset understanding and enhanced regression performance.

Findings

Partitioning reveals distinct dataset patterns like stress and strain.

Active partitioning improves regression accuracy, reducing loss by up to 54%.

Models specializing in dataset partitions outperform single models.

Abstract

Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel, general-purpose partitioning algorithm that utilizes competition between models to detect and separate these functional patterns. This competition is induced by multiple models iteratively submitting their predictions for the dataset, with the best prediction for each data point being rewarded with training on that data point. This reward mechanism amplifies each model's strengths and encourages specialization in different patterns. The specializations can then be translated into a partitioning scheme. The amplification of each model's strengths inverts the active learning paradigm: while active learning typically focuses the training of models on their weaknesses to minimize the number of required training data points, our concept reinforces the strengths of each model, thus specializing them. We validate our concept -- called active partitioning -- with various datasets with clearly distinct functional patterns, such as mechanical stress and strain data in a porous structure. The active partitioning algorithm produces valuable insights into the datasets' structure, which can serve various further applications. As a demonstration of one exemplary usage, we set up modular models consisting of multiple expert models, each learning a single partition, and compare their performance on more than twenty popular regression problems with single models learning all partitions simultaneously. Our results show significant improvements, with up to 54% loss reduction, confirming our partitioning algorithm's utility.