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swh:1:snp:3049115480bcaf552e96d5e9973040348b062b8e
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  • Description.props
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Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Tip revision: bbf7c05509a5f61dc08c0ac31f7e701eb24a51d2 authored by awojna on 15 November 2019, 16:03:19 UTC
Version changed to 3.2.3
Tip revision: bbf7c05
Description.props
# Description file of Rseslib package for Weka
#
#

# Package name (required)
PackageName=Rseslib

# Version (required)
Version=3.2.3

#Date
Date=2019-11-15

# Title (required)
Title=Rough Sets 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 3 classifiers. 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 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 third classifier LocalKnn 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.

# Package URL for obtaining the package archive (required)
PackageURL=http://rseslib.mimuw.edu.pl/weka/Rseslib3.2.3.zip

# URL for further information
URL=http://rseslib.mimuw.edu.pl

# Dependencies
Depends=weka (>=3.8.0)

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