From Classification to Regression: Using a Fruitfly to Solve Equations
2026-07-29 • Machine Learning
Machine Learning
AI summaryⓘ
The authors propose a new way to solve regression problems by turning them into classification tasks inspired by how fruitflies sense things. Instead of using one big complicated model, their method stores a limited set of typical patterns and makes predictions by comparing new inputs to these stored patterns. They apply this idea to systems that change over time and other data analysis tasks. Their approach saves computing power and memory while allowing control over accuracy and speed.
regressionclassificationnonlinear systemsdynamical systemspattern recognitionsimilarity measuresembeddingdata-driven modelingphysics-informed learning
Authors
Shady E. Ahmed, Panos Stinis
Abstract
We present a novel approach to regression tasks using classification which is motivated by the mechanism used by fruitflies to sense their environment. Specifically, we formulate a general framework for learning nonlinear input-output relationships by replacing complex global surrogate models with a finite library of representative local patterns. Since scientific data often occupy limited and recurring regions of the input space, we generate predictions by measuring similarities between a query and stored patterns, then combining their associated responses through weighted reconstruction. We apply this approach to nonlinear dynamical systems, data-driven regression, and physics-informed learning using suitable embeddings and similarity measures. For dynamical systems, our offline-online workflow extracts patterns from data or governing equations during the offline phase, while online prediction requires only similarity evaluation and response aggregation. This structure helps us reduce computational and memory demands while providing explicit control over the trade-off among accuracy, storage, and inference cost.