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eSPA plus : Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F22%3A10250372" target="_blank" >RIV/61989100:27120/22:10250372 - isvavai.cz</a>

  • Result on the web

    <a href="https://direct.mit.edu/neco/article-abstract/34/5/1220/110047/eSPA-Scalable-Entropy-Optimal-Machine-Learning?redirectedFrom=fulltext" target="_blank" >https://direct.mit.edu/neco/article-abstract/34/5/1220/110047/eSPA-Scalable-Entropy-Optimal-Machine-Learning?redirectedFrom=fulltext</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1162/neco_a_01490" target="_blank" >10.1162/neco_a_01490</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    eSPA plus : Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems

  • Original language description

    Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA). Specifically, we propose to change the order of the optimization steps and replace the most computationally expensive subproblem of eSPA with its closed-form solution. We prove that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms. On several small data learning benchmarks, we show that the eSPA+ algorithm achieves a many-fold speed-up with respect to eSPA and even better performance results when compared to a wide array of ML and DL tools. In particular, we benchmark eSPA+ against the standard eSPA and the main classes of common learning algorithms in the small data regime: various forms of support vector machines, random forests, and long short-term memory algorithms. In all the considered applications, the common learning methods and eSPA are markedly outperformed by eSPA+, which achieves significantly higher prediction accuracy with an orders-of-magnitude lower computational cost.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10103 - Statistics and probability

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Neural Computation

  • ISSN

    0899-7667

  • e-ISSN

    1530-888X

  • Volume of the periodical

    34

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    36

  • Pages from-to

    1220-1255

  • UT code for WoS article

    000785003800007

  • EID of the result in the Scopus database