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Alliance Beats Isolation: Unifying Heterogeneous Allied Datasets Improves Classifier Performance

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

In many application domains, such as student dropout, insurance fraud, loan approval, and machine failures, several labelled public datasets are available where (i) data is about the same type of objects but the set of actual underlying objects are disjoint; and (ii) the class labels are same; and (iii) the feature spaces of the datasets are largely distinct (heterogeneous), with a few shared features. We call such datasets as allied. A single classifier cannot be trained on both datasets together, and one classifier trained on one dataset cannot be tested on the other. In this paper, we propo

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