1.1k citations · 2k across the 52 of their papers we have counts for
5 papers · 1 filter
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
Rafael Gomes Mantovani, Tomáš Horváth, André L. D. Rossi +4
Machine learning algorithms often contain many hyperparameters (HPs) whose values affect the predictive performance of the induced models in intricate ways. Due to the high number…
Transformative Machine Learning
Ivan Olier, Oghenejokpeme I. Orhobor, Joaquin Vanschoren +1
The key to success in machine learning (ML) is the use of effective data representations. Traditionally, data representations were hand-crafted. Recently it has been demonstrated t…
Characterizing classification datasets: a study of meta-features for meta-learning
Adriano Rivolli, Luís P. F. Garcia, Carlos Soares +2
Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. Such recommendations are made based on meta-data, consisti…
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz +5
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing an…
Layered TPOT: Speeding up Tree-based Pipeline Optimization
Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson
With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address…