Unsupervised Ensemble Classification with Sequential and Networked Data
This work addresses the problem of handling non-i.i.d. data in ensemble learning for researchers and practitioners, though it is incremental as it extends existing methods to new data types.
The paper tackled unsupervised ensemble classification for data with dependencies, specifically sequential and networked data, by developing moment matching and Expectation Maximization algorithms, achieving performance evaluated on synthetic and real datasets.
Ensemble learning, the machine learning paradigm where multiple algorithms are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the ensemble combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most prior works on unsupervised ensemble classification are designed for independent and identically distributed (i.i.d.) data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies. Two types of data dependencies are considered: sequential data and networked data whose dependencies are captured by a graph. Moment matching and Expectation Maximization algorithms are developed for the aforementioned cases, and their performance is evaluated on synthetic and real datasets.