https://github.com/awojna/Rseslib
Tip revision: 13a923de873bdaba3cd9cfc82f46e002b7b26598 authored by awojna on 01 January 2025, 15:05:17 UTC
Version changed to 3.4.1
Version changed to 3.4.1
Tip revision: 13a923d
Description.props
# Description file of Rseslib package for Weka
#
#
# Package name (required)
PackageName=Rseslib
# Version (required)
Version=3.4.1
#Date
Date=2025-01-01
# Title (required)
Title=Rough Sets, Rule Induction and Analogy-Based Reasoning
Category=Classification
# Author (required)
Author=Arkadiusz Wojna,Grzegorz Gora,Wiktor Gromniak,Marcin Jalmuzna,Michal Kurzydlowski,Rafal Latkowski,Marcin Piliszczuk,Beata Zielosko
# Maintainer (required)
Maintainer=Arkadiusz Wojna <wojna@mimuw.edu.pl>
# License (required)
License=GPL 3.0
# Description (required)
Description=The package provides 4 classifiers: RseslibKNN, LocalKNN, RoughSet and RIONIDA. \
The k nearest neighbors classifier RseslibKNN provides variety of distance measures that can work also for data with both numeric and nominal attributes and has built-in k optimization. \
It implements a fast neighbors searching algorithm making the classifier work for very large data sets. The classifier has also the mode to work as RIONA algorithm. \
The LocalKNN classifier is the extension of the k nearest neighbors method that induces a local metric for each classified object. \
It is dedicated rather to large data sets (2000+ training instances) and improves accuracy particularly in case of data containing nominal attributes. \
The rule classifier RoughSet uses the concepts of discernibility matrix, reducts and rules generated from reducts. \
It provides variety of algorithms generating reducts including giving more general rules local reducts and has modes to work with incomplete data and inconsistent data. \
The RIONIDA classifier dedicated to imbalanced data with two decision classes combines instance-based learning with rule induction. It enables to differentiate the importance of the decisions \
and to control the impact of rules on the decision selection process and applies multi-dimensional optimization of classification measures relevant for imbalanced data.
# Package URL for obtaining the package archive (required)
PackageURL=https://github.com/awojna/Rseslib/releases/download/v3.4.1/rseslib-3.4.1-weka.zip
# URL for further information
URL=http://rseslib.mimuw.edu.pl
# Dependencies
Depends=weka (>=3.8.0)