Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Matej Petković"'
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101526- (2023)
We present CLUSplus, a machine learning framework based on decision trees specialized for complex predictive modeling tasks. We provide the scientific community with an open source Java framework that unifies several major research directions in the
Externí odkaz:
https://doaj.org/article/40b2119ce2324aefa124db07a0400147
Autor:
Matej Petković, Luke Lucas, Jurica Levatić, Martin Breskvar, Tomaž Stepišnik, Ana Kostovska, Panče Panov, Aljaž Osojnik, Redouane Boumghar, José A. Martínez-Heras, James Godfrey, Alessandro Donati, Sašo Džeroski, Nikola Simidjievski, Bernard Ženko, Dragi Kocev
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-8 (2022)
Measurement(s) electric current Technology Type(s) current readings in spacecraft housekeeping telemetry Sample Characteristic - Environment outer space
Externí odkaz:
https://doaj.org/article/f24eda5597eb4c9f93340b8f0ca19f70
Autor:
Tomaž Stepišnik, Timothy Finn, Nikola Simidjievski, Richard Southworth, Guillaume Belanger, José Antonio Martínez Heras, Matej Petković, Panče Panov, Sašo Džeroski, Alessandro Donati, Dragi Kocev
Publikováno v:
Advances in Space Research. 69:3909-3920
Publikováno v:
Trends in Food Science & Technology. 107:183-194
Background Understanding the content of self-reported meals and online-published recipes is a basic requirement for further linking food and dietary concepts to heterogeneous health networks. Despite the huge amount of work that is done in the biomed
Publikováno v:
Machine Learning. 109:2141-2159
In this paper, we propose three ensemble-based feature ranking scores for multi-label classification (MLC), which is a generalisation of multi-class classification where the classes are not mutually exclusive. Each of the scores (Symbolic, Genie3 and
Publikováno v:
Acta Polytechnica Hungarica. 17:129-148
Publikováno v:
Machine Learning Journal
The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This poses a variety of challenges for the existing machine learning metho
Publikováno v:
Machine learning
Feature ranking has been widely adopted in machine learning applications such as high-throughput biology and social sciences. The approaches of the popular Relief family of algorithms assign importances to features by iteratively accounting for neare
Autor:
Matej Petković, Michelangelo Ceci, Gianvito Pio, Blaž Škrlj, Kristian Kersting, Sašo Džeroski
Publikováno v:
Knowledge-Based Systems. 251:109254
Publikováno v:
Machine Learning. 109:1179-1204
In this work, we address the task of feature ranking for multi-target regression (MTR). The task of MTR concerns problems with multiple continuous dependent/target variables, where the goal is to learn a model for predicting all of them simultaneousl