Journal ArticleDOI
Gradient boosting for high-dimensional prediction of rare events
Rok Blagus,Lara Lusa +1 more
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TLDR
It is demonstrated that the proposed corrections successfully remove the rare events bias and outperform the other ensemble classifiers that were considered and large flexibility and high interpretability of the proposed methods is also illustrated.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2017-09-01. It has received 46 citations till now. The article focuses on the topics: Gradient boosting & Rare events.read more
Citations
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Journal ArticleDOI
Learning from class-imbalanced data
TL;DR: An in depth review of rare event detection from an imbalanced learning perspective and a comprehensive taxonomy of the existing application domains of im balanced learning are provided.
Journal ArticleDOI
A Local Adaptive Minority Selection and Oversampling Method for Class-Imbalanced Fault Diagnostics in Industrial Systems
TL;DR: The developed method uses a local-weighted minority oversampling strategy to identify hard-to-learn informative minority fault samples and an EM-based imputation algorithm to generate fault samples based on the distribution of minority samples.
Journal ArticleDOI
Machine learning for energy performance prediction at the design stage of buildings
TL;DR: It is shown that it is possible to develop a high performing ML model for building energy use prediction at the design stage and Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance.
Proceedings ArticleDOI
Improving Imbalanced Dataset Classification Using Oversampling and Gradient Boosting
TL;DR: Experiments showed that oversampling technic increase accuracy from 2% to 11% for the dataset Mammography, Liver Disorders, Diabetes (Pima Indian), Indian Liver, Habberman, and Immunotherapy, and Borderline-SMOTE increases higher accuracy compared to other oversampled method.
Journal ArticleDOI
LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test
TL;DR: A new metric based on the likelihood-ratio test, LRID, is proposed to provide a more reliable measurement of class-imbalance extent for multi-class data.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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Classification and Regression Trees.
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A Simple Sequentially Rejective Multiple Test Procedure
TL;DR: In this paper, a simple and widely accepted multiple test procedure of the sequentially rejective type is presented, i.e. hypotheses are rejected one at a time until no further rejections can be done.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.