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Found 10 papers, 3 papers with code
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their psychological validity, flexibility across problem domains and proposed legal compliance.
Random Walk-steered Majority Undersampling
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes.
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently.
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
The Radial Basis Function Data Descriptor (RBFDD) network is an effective solution for anomaly detection, however, it is a shallow model that does not deal effectively with raw data representations.
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified predictions.
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter
The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective.
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
Multi-label classification is an approach which allows a datapoint to be labelled with more than one class at the same time.
A Kalman filtering induced heuristic optimization based partitional data clustering
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches.
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network
Meetup. com is a global online platform which facilitates the organisation of meetups in different parts of the world.
Social and Information Networks Computers and Society
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem.
