most citedMeLIME: Meaningful Local Explanation for Machine Learning Models

7 citations · 7 across the 2 of their papers we have counts for

collaborators

6 papers

cs.LG2020

An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)

Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá +4

This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset.…

cs.LG20207 cited

MeLIME: Meaningful Local Explanation for Machine Learning Models

Tiago Botari, Frederik Hvilshøj, Rafael Izbicki +1

Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine…

physics.soc-ph2019

Reconstructing commuters network using machine learning and urban indicators

Gabriel Spadon, Andre C. P. L. F. de Carvalho, Jose F. Rodrigues-Jr +1

Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a…

cs.LG2019

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

Tiago Botari, Rafael Izbicki, Andre C. P. L. F. de Carvalho

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One impor…

cs.LG2019

Online Local Boosting: improving performance in online decision trees

Victor G. Turrisi da Costa, Saulo Martiello Mastelini, André C. Ponce de Leon Ferreira de Carvalho +1

As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream minin…

cs.LG2019

Online Multi-target regression trees with stacked leaf models

Saulo Martiello Mastelini, Sylvio Barbon, André Carlos Ponce de Leon Ferreira de Carvalho

One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with th…