Showing 21 of total 21 results (show query)
ncordon
imbalance:Preprocessing Algorithms for Imbalanced 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.
Maintained by Ignacio Cordón. Last updated 5 years ago.
binary-classificationimbalanced-dataoversamplingopenblascpp
22.6 match 36 stars 7.14 score 98 scripts
reddertar
smotefamily:A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
Maintained by Wacharasak Siriseriwan. Last updated 1 years ago.
7.2 match 2 stars 5.93 score 512 scripts 8 dependents
fatihsaglam
SMOTEWB:Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)
Provides the SMOTE with Boosting (SMOTEWB) algorithm. See F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a SMOTE-based resampling technique which creates synthetic data on the links between nearest neighbors. SMOTEWB uses boosting weights to determine where to generate new samples and automatically decides the number of neighbors for each sample. It is robust to noise and outperforms most of the alternatives according to Matthew Correlation Coefficient metric. Alternative resampling methods are also available in the package.
Maintained by Fatih Saglam. Last updated 2 months ago.
9.2 match 1 stars 3.77 score 13 scripts 1 dependents
cbielow
PTXQC:Quality Report Generation for MaxQuant and mzTab Results
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool). Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.
Maintained by Chris Bielow. Last updated 1 years ago.
drag-and-drophacktoberfestheatmapmatch-between-runsmaxquantmetricmztabopenmsproteomicsquality-controlquality-metricsreport
3.6 match 42 stars 9.35 score 105 scripts 1 dependents
andrisignorell
ModTools:Building Regression and Classification Models
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Maintained by Andri Signorell. Last updated 2 months ago.
5.3 match 2 stars 4.20 score 3 scripts
molson2
JOUSBoost:Implements Under/Oversampling for Probability Estimation
Implements under/oversampling for probability estimation. To be used with machine learning methods such as AdaBoost, random forests, etc.
Maintained by Matthew Olson. Last updated 8 years ago.
5.4 match 3.33 score 43 scripts
myles-lewis
nestedcv:Nested Cross-Validation with 'glmnet' and 'caret'
Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
Maintained by Myles Lewis. Last updated 7 days ago.
2.3 match 12 stars 7.92 score 46 scripts
cran
randomUniformForest:Random Uniform Forests for Classification, Regression and Unsupervised Learning
Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.
Maintained by Saip Ciss. Last updated 3 years ago.
4.7 match 3 stars 3.77 score 99 scriptsfatihsaglam
imbalanceDatRel:Relocated Data Oversampling for Imbalanced Data Classification
Relocates oversampled data from a specific oversampling method to cover area determined by pure and proper class cover catch digraphs (PCCCD). It prevents any data to be generated in class overlapping area.
Maintained by Fatih Saglam. Last updated 11 months ago.
5.5 match 2.70 score
madr0008
mldr.resampling:Resampling Algorithms for Multi-Label Datasets
Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.
Maintained by Miguel Ángel Dávila. Last updated 1 years ago.
5.2 match 1 stars 2.70 score 7 scriptscran
espadon:Easy Study of Patient DICOM Data in Oncology
Exploitation, processing and 2D-3D visualization of DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, in a patient-oriented perspective.
Maintained by Cathy Fontbonne. Last updated 1 months ago.
4.5 match 2.85 score
bioc
ILoReg:ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data
ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.
Maintained by Johannes Smolander. Last updated 5 months ago.
singlecellsoftwareclusteringdimensionreductionrnaseqvisualizationtranscriptomicsdatarepresentationdifferentialexpressiontranscriptiongeneexpression
2.3 match 5 stars 4.88 score 2 scripts
s-kganz
scutr:Balancing Multiclass Datasets for Classification Tasks
Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
Maintained by Keenan Ganz. Last updated 1 years ago.
2.8 match 2 stars 3.68 score 16 scripts 1 dependents
cefet-rj-dal
daltoolboxdp:Data Pre-Processing Extensions
An important aspect of data analytics is related to data management support for artificial intelligence. It is related to preparing data correctly. This package provides extensions to support data preparation in terms of both data sampling and data engineering. Overall, the package provides researchers with a comprehensive set of functionalities for data science based on experiment lines, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
Maintained by Eduardo Ogasawara. Last updated 3 months ago.
3.0 match 1 stars 3.26 score 12 scripts
andy-iskauskas
hmer:History Matching and Emulation Package
A set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating 'waves' of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>. Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>. Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.
Maintained by Andrew Iskauskas. Last updated 13 days ago.
1.3 match 16 stars 7.19 score 37 scripts
andrija-djurovic
PDtoolkit:Collection of Tools for PD Rating Model Development and Validation
The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different data transformation and modeling tasks such as: imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, heterogeneity, discriminatory and predictive power of the model.
Maintained by Andrija Djurovic. Last updated 1 years ago.
1.8 match 14 stars 4.78 score 86 scriptscran
FeaLect:Scores Features for Feature Selection
For each feature, a score is computed that can be useful for feature selection. Several random subsets are sampled from the input data and for each random subset, various linear models are fitted using lars method. A score is assigned to each feature based on the tendency of LASSO in including that feature in the models.Finally, the average score and the models are returned as the output. The features with relatively low scores are recommended to be ignored because they can lead to overfitting of the model to the training data. Moreover, for each random subset, the best set of features in terms of global error is returned. They are useful for applying Bolasso, the alternative feature selection method that recommends the intersection of features subsets.
Maintained by Habil Zare. Last updated 5 years ago.
1.8 match 2 stars 3.44 score 23 scripts
kjhealy
gssrdoc:Document General Social Survey Variable
The General Social Survey (GSS) is a long-running, mostly annual survey of US households. It is administered by the National Opinion Research Center (NORC). This package contains the a tibble with information on the survey variables, together with every variable documented as an R help page. For more information on the GSS see \url{http://gss.norc.org}.
Maintained by Kieran Healy. Last updated 11 months ago.
2.0 match 2.28 score 38 scripts
nkathh
sambia:A Collection of Techniques Correcting for Sample Selection Bias
A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.
Maintained by Norbert Krautenbacher. Last updated 7 years ago.
2.2 match 1.18 score 15 scripts
hopfkons
SmartMeterAnalytics:Methods for Smart Meter Data Analysis
Methods for analysis of energy consumption data (electricity, gas, water) at different data measurement intervals. The package provides feature extraction methods and algorithms to prepare data for data mining and machine learning applications. Deatiled descriptions of the methods and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> "Enhancing energy efficiency in the residential sector with smart meter data analytics".
Maintained by Konstantin Hopf. Last updated 5 years ago.
1.9 match 1.00 score