A Caltech Library Service

Analytic Continued Fractions for Regression: Results on 352 datasets from the physical sciences

Moscato, Pablo and Sun, Haoyuan and Haque, Mohammad Nazmul (2020) Analytic Continued Fractions for Regression: Results on 352 datasets from the physical sciences. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE , Piscataway, NJ, pp. 1-8. ISBN 9781728169293.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


We report on the results of a new memetic algorithm that employs analytic continued fractions as the basic representation of mathematical functions used for regression problems. We study the performance of our method in comparison with other ten machine learning approaches provided by the scikit-learn software collection. We used 352 datasets collected by Schaffer, which originated from real experiments in the physical sciences at the turn of the 20 th century for which measurements were tabulated, and a governing functional relationship was postulated. Using leave-one-out cross-validation, in training our method ranks first in 350 out of the 352 datasets. Only six machine learning algorithms ranked first in at least one of the 352 datasets on testing; our approach ranked first 192 times, i.e. more all of the other algorithms combined. The results favourably speak about the robustness of our methodology. We conclude that the use of analytic continued fractions in regression deserves further study and we also advocate that Schaffer's data collection should also be included in the repertoire of datasets to test the performance of machine learning and regression algorithms.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Moscato, Pablo0000-0003-2570-5966
Haque, Mohammad Nazmul0000-0002-0598-0867
Additional Information:© 2020 IEEE. We thank Markus Wagner for his thoughtful comments on an earlier version of the manuscript. Work supported by UoN, Caltech SURF, Maitland Cancer Appeal and Australian Research Council Discovery Project, DP200102364.
Funding AgencyGrant Number
University of NewcastleUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Maitland Cancer AppealUNSPECIFIED
Australian Research CouncilDP200102364
Subject Keywords:memetic computing, regression, analytic continued fraction
Record Number:CaltechAUTHORS:20201209-153307465
Persistent URL:
Official Citation:P. Moscato, H. Sun and M. N. Haque, "Analytic Continued Fractions for Regression: Results on 352 datasets from the physical sciences," 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, United Kingdom, 2020, pp. 1-8, doi: 10.1109/CEC48606.2020.9185564
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106991
Deposited By: George Porter
Deposited On:10 Dec 2020 15:01
Last Modified:16 Nov 2021 18:58

Repository Staff Only: item control page