@inproceedings{cb0642ef768e411180a3c9c3e70b7d9d,
title = "On Positive-Correlation-Promoting Reducts",
abstract = "We introduce a new rough-set-inspired binary feature selection framework, whereby it is preferred to choose attributes which let us distinguish between objects (cases, rows, examples) having different decision values according to the following mechanism: for objects u1 and u2 with decision values and, it is preferred to select attributes a such that and, with the secondary option {\textendash} if the first one is impossible {\textendash} to select a such that and. We discuss the background for this approach, originally inspired by the needs of the genetic data analysis. We show how to derive the sets of such attributes {\textendash} called positive-correlation-promoting reducts (PCP reducts in short) {\textendash} using standard calculations over appropriately modified rough-set-based discernibility matrices. The proposed framework is implemented within the RoughSets R package which is widely used for the data exploration and knowledge discovery purposes.",
keywords = "Discernibility, Feature selection, Positive-correlation-promoting reducts, Rough sets, RoughSets R package, Rule induction",
author = "Joanna Henzel and Andrzej Janusz and Marek Sikora and Dominik {\'S}l{\c e}zak",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Joint Conference on Rough Sets, IJCRS 2020 ; Conference date: 29-06-2020 Through 03-07-2020",
year = "2020",
doi = "10.1007/978-3-030-52705-1\_16",
language = "English",
isbn = "9783030527044",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "213--221",
editor = "Rafael Bello and Duoqian Miao and Rafael Falcon and Michinori Nakata and Alejandro Rosete and Davide Ciucci",
booktitle = "Rough Sets - International Joint Conference, IJCRS 2020, Proceedings",
address = "Germany",
}