30 citations · 31 across the 6 of their papers we have counts for
8 papers
Random Walk-steered Majority Undersampling
Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee
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
Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit +1
There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significa…
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
Mehran H. Z. Bazargani, Arjun Pakrashi, Brian Mac Namee
Anomaly detection is a challenging problem in machine learning, and is even more so when dealing with instances that are captured in low-level, raw data representations without a w…
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
John Mitros, Arjun Pakrashi, Brian Mac Namee
Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified pre…
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
Arjun Pakrashi, Brian Mac Namee
Multi-label classification is an approach which allows a datapoint to be labelled with more than one class at the same time. A common but trivial approach is to train individual bi…
A Kalman filtering induced heuristic optimization based partitional data clustering
Arjun Pakrashi, Bidyut B. Chaudhuri
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, vario…