banana

banana_orig

Dataset containing two attributes as well as a class one, that, if plotted, represent
a banana shape

datasets

Class imbalance usually damages the performance of classifiers. Thus, it is
important to treat data before applying a classifier algorithm. This package
includes recent resampling algorithms in the literature: (Barua et al. 2014)
<doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>,
(Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014)
<doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014)
<doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to
perform oversampling.

Ignacio Cord<c3><b3>n

imbalance

Preprocessing Algorithms for Imbalanced Datasets

Salvador Garc<c3><ad>a

Alberto Fern<c3><a1>ndez

Francisco Herrera

banana function

<dl class="dl-horizontal">
 <dt>At1</dt><dd>First attribute.</dd>
 <dt>At2</dt><dd>Second attribute.</dd>
 <dt>Class</dt><dd>Two possible classes: positive (banana shape), negative (surrounding
 of the banana).</dd>
</dl>

Format

<code>banana</code>: A data frame with 2640 instances, 264 of which belong to positive class,
and 3 variables<code>banana_orig</code>: A data frame with 5300 instances, 2376 of which belong to positive
 class, and 3 variables:

Shape

Binary banana dataset — banana

<dl class='dl-horizontal'>
 <dt>At1</dt><dd>First attribute.</dd>
 <dt>At2</dt><dd>Second attribute.</dd>
 <dt>Class</dt><dd>Two possible classes: positive (banana shape), negative (surrounding
 of the banana).</dd>
</dl>

banana: Binary banana dataset

Description

Usage

Arguments

Format

Shape