• ADASYN

  • Referenced in 19 articles [sw36457]
  • ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN...
  • SMOTE

  • Referenced in 152 articles [sw34239]
  • imbalanced if the classification categories are not approximately equally represented. Often real-world data sets...
  • MWMOTE

  • Referenced in 14 articles [sw32596]
  • Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning. Imbalanced learning problems contain ... TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard ... four artificial and 20 real-world data sets. The simulation results show that our method...
  • robROSE

  • Referenced in 1 article [sw41392]
  • robust approach for dealing with imbalanced data in fraud detection. A major challenge when trying ... very small proportion of the data set. In most data sets, fraud occurs in typically ... Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor ... techniques that solve the problem of imbalanced data by creating synthetic samples that mimic...
  • hyperSMURF

  • Referenced in 0 articles [sw16396]
  • learn rare genomic features in imbalanced genetic data sets. This method can be also applied ... minority class to learn highly imbalanced data. Both single-core and parallel multi-core version...
  • IRIC

  • Referenced in 2 articles [sw32593]
  • binary imbalanced classification. Imbalanced classification is a challenging issue in data mining and machine learning ... IRIC, which integrates a wide set of solutions for imbalanced binary classification. IRIC not only...
  • ACOSampling

  • Referenced in 5 articles [sw41786]
  • method for classifying imbalanced DNA microarray data. In DNA microarray data, class imbalance problem occurs ... genes in data. Then we randomly and repeatedly divided the original training set into...
  • SackinMinimizer

  • Referenced in 7 articles [sw39018]
  • ones allow for more efficient data structuring than imbalanced ones. Therefore, different methods to measure ... both in the binary and non-binary settings. All our results have been implemented...
  • LCE

  • Referenced in 5 articles [sw25277]
  • parameterization, for a particular set of gene expression data, because there are a very large ... solves this problem by automatically combining multiple data partitions from different clusterings to improve both ... capability of the ensemble methodology for microarray data clustering. RESULTS: The link-based cluster ensemble ... different types of data, especially with the presence of noise and imbalanced data clusters...
  • Maple

  • Referenced in 5369 articles [sw00545]
  • The result of over 30 years of cutting...
  • MapReduce

  • Referenced in 263 articles [sw00546]
  • MapReduce is a new parallel programming model initially...
  • Matlab

  • Referenced in 13488 articles [sw00558]
  • MATLAB® is a high-level language and interactive...
  • mclust

  • Referenced in 308 articles [sw00563]
  • R package mclust: Normal Mixture Modeling for Model...
  • R

  • Referenced in 9832 articles [sw00771]
  • R is a language and environment for statistical...
  • RFCM

  • Referenced in 8 articles [sw02668]
  • RFCM: a hybrid clustering algorithm using rough and...
  • SeDuMi

  • Referenced in 1272 articles [sw04002]
  • SeDuMi is a Matlab toolbox for solving optimization...
  • SDPT3

  • Referenced in 697 articles [sw04009]
  • This software is designed to solve conic programming...
  • UCI-ml

  • Referenced in 3397 articles [sw04074]
  • UC Irvine Machine Learning Repository. We currently maintain...
  • SVMlight

  • Referenced in 264 articles [sw04076]
  • Description (homepage): SVMlight is an implementation of Vapnik...