48 citations · 50 across the 3 of their papers we have counts for
5 papers
On-the-Fly Ensemble Pruning in Evolving Data Streams
Sanem Elbasi, Alican Büyükçakır, Hamed Bonab +1
Ensemble pruning is the process of selecting a subset of componentclassifiers from an ensemble which performs at least as well as theoriginal ensemble while reducing storage and co…
Cross-Market Product Recommendation
Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi +2
We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypo…
A Multi-Task Architecture on Relevance-based Neural Query Translation
Sheikh Muhammad Sarwar, Hamed Bonab, James Allan
We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translati…
A Novel Online Stacked Ensemble for Multi-Label Stream Classification
Alican Büyükçakır, Hamed Bonab, Fazli Can
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a su…
GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams
Hamed R. Bonab, Fazli Can
Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an on…