Repositorio para los proyectos de la asignatura Sistemas Inteligentes para la Gestión en la Empresa (Business Intilligence) del master profesional en ingeniería informática de la Universidad de Granada.
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Updated
Mar 27, 2017 - R
Repositorio para los proyectos de la asignatura Sistemas Inteligentes para la Gestión en la Empresa (Business Intilligence) del master profesional en ingeniería informática de la Universidad de Granada.
Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
SMOGN: a Pre-processing Approach for Imbalanced Regression - LIDTA2017
Text classification with scikit-learn, used to make predictions for Kaggle Spooky Author Identification competition
Machine Learning Project on Imbalanced Data in R
Sampling Algorithms for Two-Class Imbalanced Data Sets in R
Master Thesis
Theano implementation of Cost-Sensitive Deep Neural Networks
labs for PRML
Extended cross validation, feature selection methods for imbalanced data analysis
Kaggle Challenge
Develop a knowledge-based approach using MSK-IMPACT data to build an automatic variant classifier
Como resolver o problema de classificação com dados de diferentes tamanhos
We explore three algorithms and their combinations to tackle this problem of imbalance in the given dataset. Those algorithms are Gradient Boosting, SMOTE and Tomek Links. Each one is discussed in its own section.
Multivariate Normal Distribution based Oversampling
A new metric to measure multi-class imbalance degree of data using likelihood ratio test based on the paper LRID by Rui Zhu et. al.
SOUL: Scala Oversampling and Undersampling Library.
P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.
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