Publications
Dataset · November 25, 2019
abcrlda: Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis
Dmitriy Fedorov, Amin Zollanvari, Aresh Dadlani, Berdakh Abibullaev
Abstract
Offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. The bias-correction is an estimate of the bias term added to regularized discriminant analysis (RLDA) that minimizes the overall risk. The default magnitude of misclassification costs are equal and set to 0.5; however, the package also offers the options to set them to some predetermined values or, alternatively, take them as hyperparameters to tune. A. Zollanvari, M. Abdirash, A. Dadlani and B. Abibullaev (2019) <doi:10.1109/LSP.2019.2918485>.
Statistical Methods and ApplicationsImbalanced Data Classification TechniquesStatistical Methods and Inferenceregularized linear discriminant analysisbias correctioncost-sensitive classification
More by Aresh Dadlani
- Joint Freshness and Age-Dispersion Control over Finite-State Markov Wireless ChannelsarXiv · 2026
- Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural NetworksarXiv · 2026
- DeepIrrigo: Deep Reinforcement Learning for Continuous and Discrete Action Irrigation Optimization--A Case Study in IranSSRN Electronic Journal · 2026
- Age Performance Analysis in Resource-Constrained Adversarial IoT SystemsarXiv · 2025