Binary banana dataset

Description

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

Usage

banana

banana_orig

Format

At1

First attribute.

At2

Second attribute.

Class

Two possible classes: positive (banana shape), negative (surrounding of the banana).

Shape

banana: A data frame with 2640 instances, 264 of which belong to positive class, and 3 variables

banana_orig: A data frame with 5300 instances, 2376 of which belong to positive class, and 3 variables:

Source

KEEL Repository.

Imbalanced binary ecoli protein localization sites

Description

Imbalanced binary dataset containing protein traits for predicting their cellular localization sites.

Usage

ecoli1

Format

A data frame with 336 instances, 77 of which belong to positive class, and 8 variables:

Mcg

McGeoch's method for signal sequence recognition. Continuous attribute.

Gvh

Von Heijne's method for signal sequence recognition. Continuous attribute.

Lip

von Heijne's Signal Peptidase II consensus sequence score. Discrete attribute.

Chg

Presence of charge on N-terminus of predicted lipoproteins. Discrete attribute.

Aac

Score of discriminant analysis of the amino acid content of outer membrane and periplasmic proteins. Continuous attribute.

Alm1

Score of the ALOM membrane spanning region prediction program. Continuous attribute.

Alm2

score of ALOM program after excluding putative cleavable signal regions from the sequence. Continuous attribute.

Class

Two possible classes: positive (type im), negative (the rest).

Source

KEEL Repository.

See Also

Original available in UCI ML Repository.

Imbalanced binary glass identification

Description

Imbalanced binary classification dataset containing variables to identify types of glass.

Usage

glass0

Format

A data frame with 214 instances, 70 of which belong to positve class, and 10 variables:

RI

Refractive Index. Continuous attribute.

Na

Sodium, weight percent in component. Continuous attribute.

Mg

Magnesium, weight percent in component. Continuous attribute.

Al

Aluminum, weight percent in component. Continuous attribute.

Si

Silicon, weight percent in component. Continuous attribute.

K

Potasium, weight percent in component. Continuous attribute.

Ca

Calcium, weight percent in component. Continuous attribute.

Ba

Barium, weight percent in component. Continuous attribute.

Fe

Iron, weight percent in component. Continuous attribute.

Class

Two possible glass types: positive (building windows, float processed) and negative (the rest).

Source

KEEL Repository.

See Also

Original available in UCI ML Repository.

Haberman's survival data

Description

The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.

Usage

haberman

Format

A data frame with 306 instances, 81 of which belong to positive class, and 4 variables:

Age

Age of patient at time of operation. Discrete attribute.

Year

Patient's year of operation. Discrete attribute.

Positive

Number of positive axillary nodes detected. Discrete attribute.

Class

Two possible survival status: positive(survival rate of less than 5 years), negative (survival rate or more than 5 years).

Source

KEEL Repository.

See Also

Original available in UCI ML Repository.

imabalance: A package to treat imbalanced datasets

Description

Focused on binary class datasets, the imbalance package provides methods to generate synthetic examples and achieve balance between the minority and majority classes in dataset distributions

Oversampling

Methods to oversample the minority class: racog, wracog, rwo, pdfos, mwmote

Evaluation

Method to measure imbalance ratio in a given two-class dataset: imbalanceRatio.

Method to visually evaluate algorithms: plotComparison.

Filtering

Methods to filter oversampled instances neater.

Compute imbalance ratio of a binary dataset

Description

Given a two-class dataset, it computes its imbalance ratio as {Size of minority class}/{Size of majority class}

Usage

imbalanceRatio(dataset, classAttr = "Class")

Arguments

Value

A real number in [0,1] representing the imbalance ratio of dataset

Examples

data(glass0)

imbalanceRatio(glass0, classAttr = "Class")

Imbalanced binary iris dataset

Description

Modification of iris dataset. Measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The possible classifications are positive (setosa) and negative (versicolor + virginica).

Usage

iris0

Format

A data frame with 150 instances, 50 of which belong to positive class, and 5 variables:

SepalLength

Measurement of sepal length, in cm. Continuous attribute.

SepalWidth

Measurement of sepal width, in cm. Continuous attribute.

PetalLength

Measurement of petal length, in cm. Continuous attribute.

PetalWidth

Measurement of petal width, in cm. Continuous attribute.

Class

Two possible classes: positive (setosa) and negative (versicolor + virginica).

Source

KEEL Repository.

Majority weighted minority oversampling technique for imbalance dataset learning

Description

Modification for SMOTE technique which overcomes some of the problems of the SMOTE technique when there are noisy instances, in which case SMOTE would generate more noisy instances out of them.

Usage

mwmote(
  dataset,
  numInstances,
  kNoisy = 5,
  kMajority = 3,
  kMinority,
  threshold = 5,
  cmax = 2,
  cclustering = 3,
  classAttr = "Class"
)

Arguments

Value

A data.frame with the same structure as dataset, containing the generated synthetic examples.

References

Barua, Sukarna; Islam, Md.M.; Yao, Xin; Murase, Kazuyuki. Mwmote–majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning. IEEE Transactions on Knowledge and Data Engineering 26 (2014), Nr. 2, p. 405–425

Examples

data(iris0)

# Generates new minority examples
newSamples <- mwmote(iris0, numInstances = 100, classAttr = "Class")

Fitering of oversampled data based on non-cooperative game theory

Description

Filters oversampled examples from a binary class dataset using game theory to find out if keeping an example is worthy enough.

Usage

neater(
  dataset,
  newSamples,
  k = 3,
  iterations = 100,
  smoothFactor = 1,
  classAttr = "Class"
)

Arguments

Details

Uses game theory and Nash equilibriums to calculate the minority examples probability of trully belonging to the minority class. It discards examples which at the final stage of the algorithm have more probability of being a majority example than a minority one.

Value

Filtered samples as a data.frame with same structure as newSamples.

References

Almogahed, B.A.; Kakadiaris, I.A. Neater: Filtering of Over-Sampled Data Using Non-Cooperative Game Theory. Soft Computing 19 (2014), Nr. 11, p. 3301–3322.

Examples

data(iris0)

newSamples <- smotefamily::SMOTE(iris0[,-5], iris0[,5])$syn_data
# SMOTE overrides Class attr turning it into class
# and dataset must have same class attribute as newSamples
names(newSamples) <- c(names(newSamples)[-5], "Class")

neater(iris0, newSamples, k = 5, iterations = 100,
       smoothFactor = 1, classAttr = "Class")

Imbalanced binary thyroid gland data

Description

Data to predict patient's hyperthyroidism.

Usage

newthyroid1

Format

A data frame with 215 instances, 35 of which belong to positive class, and 6 variables:

T3resin

T3-resin uptake test, percentage. Discrete attribute.

Thyroxin

Total Serum thyroxin as measured by the isotopic displacement method. Continuous attribute.

Triiodothyronine

Total serum triiodothyronine as measured by radioimmuno assay. Continuous attribute.

Thyroidstimulating

Basal thyroid-stimulating hormone (TSH) as measured by radioimmuno assay. Continuous attribute.

TSH_value

Maximal absolute difference of TSH value after injection of 200 micro grams of thyrotropin-releasing hormone as compared to the basal value. Continuous attribute.

Class

Two possible classes: positive as hyperthyroidism, negative as non hyperthyroidism.

Source

KEEL Repository.

See Also

Original available in UCI ML Repository.

Wrapper that encapsulates a collection of algorithms to perform a class balancing preprocessing task for binary class datasets

Description

Wrapper that encapsulates a collection of algorithms to perform a class balancing preprocessing task for binary class datasets

Usage

oversample(
  dataset,
  ratio = NA,
  method = c("RACOG", "wRACOG", "PDFOS", "RWO", "ADASYN", "ANSMOTE", "SMOTE", "MWMOTE",
    "BLSMOTE", "DBSMOTE", "SLMOTE", "RSLSMOTE"),
  filtering = FALSE,
  classAttr = "Class",
  wrapper = c("KNN", "C5.0"),
  ...
)

Arguments

Value

A balanced data.frame with same structure as dataset, containing both original instances and new ones

Examples

data(glass0)

# Oversample glass0 to get an imbalance ratio of 0.8
imbalanceRatio(glass0)
# 0.4861111
newDataset <- oversample(glass0, ratio = 0.8, method = "MWMOTE")
imbalanceRatio(newDataset)
newDataset <- oversample(glass0, method = "ADASYN")
newDataset <- oversample(glass0, ratio = 0.8, method = "SMOTE")

Probability density function estimation based oversampling

Description

Generates synthetic minority examples for a numerical dataset approximating a Gaussian multivariate distribution which best fits the minority data.

Usage

pdfos(dataset, numInstances, classAttr = "Class")

Arguments

Details

To generate the synthetic data, it approximates a normal distribution with mean a given example belonging to the minority class, and whose variance is the minority class variance multiplied by a constant; that constant is computed so that it minimizes the mean integrated squared error of a Gaussian multivariate kernel function.

Value

A data.frame with the same structure as dataset, containing the generated synthetic examples.

References

Gao, Ming; Hong, Xia; Chen, Sheng; Harris, Chris J.; Khalaf, Emad. Pdfos: Pdf Estimation Based Oversampling for Imbalanced Two-Class Problems. Neurocomputing 138 (2014), p. 248–259

Silverman, B. W. Density Estimation for Statistics and Data Analysis. Chapman & Hall, 1986. – ISBN 0412246201

Examples

data(iris0)

newSamples <- pdfos(iris0, numInstances = 100, classAttr = "Class")

Plots comparison between the original and the new balanced dataset.

Description

It plots a grid of one to one variable comparison, placing the former dataset graphics next to the balanced one, for each pair of attributes.

Usage

plotComparison(dataset, anotherDataset, attrs, cols = 2, classAttr = "Class")

Arguments

Value

Plot of 2D comparison between the variables.

Examples

data(iris0)
set.seed(12345)

rwoSamples <- rwo(iris0, numInstances = 100)
rwoBalanced <- rbind(iris0, rwoSamples)
plotComparison(iris0, rwoBalanced, names(iris0), cols = 2, classAttr = "Class")

Rapidly converging Gibbs algorithm.

Description

Allows you to treat imbalanced discrete numeric datasets by generating synthetic minority examples, approximating their probability distribution.

Usage

racog(dataset, numInstances, burnin = 100, lag = 20, classAttr = "Class")

Arguments

Details

Approximates minority distribution using Gibbs Sampler. Dataset must be discretized and numeric. In each iteration, it builds a new sample using a Markov chain. It discards first burnin iterations, and from then on, each lag iterations, it validates the example as a new minority example. It generates d (iterations-burnin)/lag where d is minority examples number.

Value

A data.frame with the same structure as dataset, containing the generated synthetic examples.

References

Das, Barnan; Krishnan, Narayanan C.; Cook, Diane J. Racog and Wracog: Two Probabilistic Oversampling Techniques. IEEE Transactions on Knowledge and Data Engineering 27(2015), Nr. 1, p. 222–234.

Examples

data(iris0)

# Generates new minority examples

newSamples <- racog(iris0, numInstances = 40, burnin = 20, lag = 10,
                    classAttr = "Class")

newSamples <- racog(iris0, numInstances = 100)

Random walk oversampling

Description

Generates synthetic minority examples for a dataset trying to preserve the variance and mean of the minority class. Works on every type of dataset.

Usage

rwo(dataset, numInstances, classAttr = "Class")

Arguments

Details

Generates numInstances new minority examples for dataset, adding to the each numeric column of the j-th example its variance scalated by the inverse of the number of minority examples and a factor following a N(0,1) distribution which depends on the example. When the column is nominal, it uses a roulette scheme.

Value

A data.frame with the same structure as dataset, containing the generated synthetic examples.

References

Zhang, Huaxiang; Li, Mingfang. Rwo-Sampling: A Random Walk Over-Sampling Approach To Imbalanced Data Classification. Information Fusion 20 (2014), p. 99–116.

Examples

data(iris0)

newSamples <- rwo(iris0, numInstances = 100, classAttr = "Class")

Generic methods to train classifiers

Description

Generic methods to train classifiers

Usage

trainWrapper(wrapper, train, trainClass, ...)

Arguments

Value

A model which is predict callable.

See Also

predict

Examples

myWrapper <- structure(list(), class="C50Wrapper")
trainWrapper.C50Wrapper <- function(wrapper, train, trainClass){
  C50::C5.0(train, trainClass)
}

Imbalanced binary breast cancer Wisconsin dataset

Description

Binary class dataset containing traits about patients with cancer. Original dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

Usage

wisconsin

Format

A data frame with 683 instances, 239 of which belong to positive class, and 10 variables:

ClumpThickness

Discrete attribute.

CellSize

Discrete attribute.

CellShape

Discrete attribute.

MarginalAdhesion

Discrete attribute.

EpithelialSize

Discrete attribute.

BareNuclei

Discrete attribute.

BlandChromatin

Disrete attribute.

NormalNucleoli

Discrete attribute.

Mitoses

Discrete attribute.

Class

Two possible classes: positive (cancer) and negative (not cancer).

Source

KEEL Repository.

See Also

Original available in UCI ML Repository.

Wrapper for rapidly converging Gibbs algorithm.

Description

Generates synthetic minority examples by approximating their probability distribution until sensitivity of wrapper over validation cannot be further improved. Works only on discrete numeric datasets.

Usage

wracog(
  train,
  validation,
  wrapper,
  slideWin = 10,
  threshold = 0.02,
  classAttr = "Class",
  ...
)

Arguments

Details

Until the last slideWin executions of wrapper over validation dataset reach a mean sensitivity lower than threshold, the algorithm keeps generating samples using Gibbs Sampler, and adding misclassified samples with respect to a model generated by a former train, to the train dataset. Initial model is built on initial train.

Value

A data.frame with the same structure as train, containing the generated synthetic examples.

References

Das, Barnan; Krishnan, Narayanan C.; Cook, Diane J. Racog and Wracog: Two Probabilistic Oversampling Techniques. IEEE Transactions on Knowledge and Data Engineering 27(2015), Nr. 1, p. 222–234.

Examples

data(haberman)

# Create train and validation partitions of haberman
trainFold <- sample(1:nrow(haberman), nrow(haberman)/2, FALSE)
trainSet <- haberman[trainFold, ]
validationSet <- haberman[-trainFold, ]

# Defines our own wrapper with a C5.0 tree
myWrapper <- structure(list(), class="TestWrapper")
trainWrapper.TestWrapper <- function(wrapper, train, trainClass){
  C50::C5.0(train, trainClass)
}

# Execute wRACOG with our own wrapper
newSamples <- wracog(trainSet, validationSet, myWrapper,
                     classAttr = "Class")


# Execute wRACOG with predifined wrappers for "KNN" or "C5.0"
KNNSamples <- wracog(trainSet, validationSet, "KNN")
C50Samples <- wracog(trainSet, validationSet, "C5.0")

Imbalanced binary yeast protein localization sites

Description

Imbalanced binary dataset containing protein traits for predicting their cellular localization sites.

Usage

yeast4

Format

A data frame with 1484 instances, 51 of which belong to positive class, and 9 variables:

Mcg

McGeoch's method for signal sequence recognition. Continuous attribute.

Gvh

Von Heijne's method for signal sequence recognition. Continuous attribute.

Alm

Score of the ALOM membrane spanning region prediction program. Continuous attribute.

Mit

Score of discriminant analysis of the amino acid content of the N-terminal region (20 residues long) of mitochondrial and non-mitochondrial proteins. Continuous attribute.

Erl

Presence of "HDEL" substring (thought to act as a signal for retention in the endoplasmic reticulum lumen). Binary attribute. Discrete attribute.

Pox

Peroxisomal targeting signal in the C-terminus. Continuous attribute.

Vac

Score of discriminant analysis of the amino acid content of vacuolar and extracellular proteins. Continuous attribute.

Nuc

Score of discriminant analysis of nuclear localization signals of nuclear and non-nuclear proteins. Continuous attribute.

Class

Two possible classes: positive (membrane protein, uncleaved signal), negative (rest of localizations).

Source

KEEL Repository.

See Also

Original available in UCI ML Repository.