Designing machine learning workflows with an application to topological data analysis


Autoři: Eric Cawi aff001;  Patricio S. La Rosa aff002;  Arye Nehorai aff001
Působiště autorů: Preston M. Green Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, United States of America aff001;  Global IT Analytics, Crop Science Division, Bayer Company, Saint Louis, MO, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225577

Souhrn

In this paper we define the concept of the Machine Learning Morphism (MLM) as a fundamental building block to express operations performed in machine learning such as data preprocessing, feature extraction, and model training. Inspired by statistical learning, MLMs are morphisms whose parameters are minimized via a risk function. We explore operations such as composition of MLMs and when sets of MLMs form a vector space. These operations are used to build a machine learning workflow from data preprocessing to final task completion. We examine the Mapper Algorithm from Topological Data Analysis as an MLM, and build several workflows for binary classification incorporating Mapper on Hospital Readmissions and Credit Evaluation datasets. The advantage of this framework lies in the ability to easily build, organize, and compare multiple workflows, and allows joint optimization of parameters across multiple steps in an application.

Klíčová slova:

Algorithms – Machine learning – Machine learning algorithms – Optimization – principal component analysis – Support vector machines – Vector spaces


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Článek vyšel v časopise

PLOS One


2019 Číslo 12