Integrated Fisher linear discriminants: an empirical study

DOI10.1016/j.patcog.2013.07.021zbMath1326.68219OpenAlexW2041162724MaRDI QIDQ898334

Gao Daqi, Zhu Changming, Ding Jun

Publication date: 8 December 2015

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2013.07.021

zbMATH Keywords

iterative learningempirical thresholdsFisher linear discriminantsimbalanced datasetsneighborhood-preserving transformations

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (3)

Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems ⋮ Pseudo-inverse linear discriminants for the improvement of overall classification accuracies ⋮ Fuzzy granular gravitational clustering algorithm for multivariate data

Uses Software

• UCI-ml
• HandTill2001
• MNIST

Cites Work

• Unnamed Item
• Unnamed Item
• Learning noisy linear classifiers via adaptive and selective sampling
• Resampling approach for cluster model selection
• Novel Fisher discriminant classifiers
• Multi-class pattern classification using neural networks
• Cost-sensitive boosting for classification of imbalanced data
• A \(k\)-norm pruning algorithm for decision tree classifiers based on error rate estimation
• Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.
• Structured large margin machines: sensitive to data distributions
• Do unbalanced data have a negative effect on LDA?
• Considerations of sample and feature size
• A simple generalisation of the area under the ROC curve for multiple class classification problems

This page was built for publication: Integrated Fisher linear discriminants: an empirical study