Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry

08 July 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The appropriate sampling of training data out of a potentially imbalanced data set is of critical importance for the development of robust and accurate machine learning models. A challenge that underpins this task is the partitioning of the data into groups of similar instances, and the analysis of the group populations. In molecular data sets, different groups of molecules may be hard to identify. However, if the distribution of a given data set is ignored then some of these groups may remain under-represented and the sampling biased, even if the size of data is large. In this study, we use the example of the Harvard Clean Energy Project (CEP) data set to assess the challenges posed by imbalanced data and the impact that accounting for different groups during the selection of training data has on the quality of the resulting machine learning models. We employ a partitioning criterion based on the underlying rules for the CEP molecular library generation to identify groups of structurally similar compounds. First, we evaluate the performance of regression models that are trained globally (i.e., by randomly sampling the entire data set for training data). This traditional approach serves as the benchmark reference. We compare its results with those of models that are trained locally, i.e., within each of the identified molecular domains. We demonstrate that local models outperform even the best global models by considerable margins and are more efficient in their training data needs. We propose a strategy to redesign training sets for the development of improved global models. While the resulting uniform training sets can successfully yield robust global models, we identify the distribution mismatch between feature representations of different molecular domains as a critical limitation for any further improvement. We take advantage of the discovered distribution shift and propose an ensemble of classification and regression models to achieve generalized and reliable models across the CEP data set. This study provides a benchmark for the development of future methodologies concerned with imbalanced chemical data.

Keywords

data Mining
imbalanced data sets
photovoltaic
clean energy
machine learning-based

Supplementary materials

Title
Description
Actions
Title
43 fragfps ml cep supp
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.