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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead.

Sarah E Marzen¹, Paul M Riechers², James P Crutchfield³

¹W. M. Keck Science Department of Pitzer, Scripps, and Claremont McKenna College, Claremont, CA, 91711, USA. smarzen@cmc.edu.

Scientific Reports

|April 15, 2024

Summary

Next-generation reservoir computers, a type of recurrent neural network, struggle with complex time series prediction. New architectures are needed for optimal performance in forecasting financial and climate data.

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Area of Science:

Computational Neuroscience
Machine Learning
Time Series Analysis

Background:

Recurrent neural networks (RNNs) are widely used for time series forecasting across various domains like finance, climate, and language.
Reservoir computers are a simplified, easily trainable class of RNNs.
A recent 'next-generation' reservoir computer model features a finite-past memory trace.

Purpose of the Study:

To investigate the inherent limitations of finite-past memory traces in next-generation reservoir computers.
To assess the performance of current RNNs in predicting complex, non-Markovian processes.
To identify requirements for future RNN architectures.

Main Methods:

Utilized Fano's inequality to establish a lower bound on prediction error for next-generation reservoir computers.
Analyzed highly non-Markovian processes generated by large probabilistic state machines.
Presented concentration-of-measure results for complex, randomly generated processes.

Main Results:

Next-generation reservoir computers exhibit a prediction error significantly higher than the theoretical minimum for complex processes.
Popular RNNs demonstrate suboptimal performance in predicting intricate time series.
Large probabilistic state machines, particularly -machines, are crucial for generating unbiased training data.

Conclusions:

Finite-past memory traces impose fundamental limitations on reservoir computer prediction accuracy.
Current RNNs are not optimized for highly complex, non-Markovian time series.
Development of novel, optimized RNN architectures is essential.
Large probabilistic state machines serve as vital benchmarks for evaluating RNNs.