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Although the current performance of the unsupervised solution does not adequately replace manual engineering, we believe once the performance issues are addressed, it could serve as an aide in a semi-supervised solution.
Li et al. construct a supervised classifier on top of these features to provide a semi-supervised solution to a majority of unlabeled data reaching upto an F1 score of 0.631.
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Tsai et al. [46] proposed a semi-supervised HDA solution called Cross-Domain Landmark Selection (CDLS).
Hence, Co-MIML is a semi-supervised version solution, and in our experiments, we compare all methods in a semi-supervised scenario.
The paper proposes a new constrained self-organizing map (SOM) to combine multiple semi-supervised clustering solutions for further enhancing the performance of ICop-Kmeans.
Semi-supervised learning solutions are deployed here, able to access reference data when it's available, and use unsupervised learning techniques to make "best guesses" when it comes to filling in gaps.
As opposed to prior work, in this work, our goal is to investigate ensemble-based semi-supervised learning as a potential solution for splice site prediction and to study the effects of imbalanced distributions on semi-supervised algorithms when labeled data is sparse.
A small number of studies [ 32, 26, 33] have explored the data imbalance problem in the semi-supervised context and proposed effective solutions, but the imbalance degrees were moderate.
Their solution relies on semi-supervised and active learning algorithms based on Gaussian mixture models.
Inspired by weak label learning and semi-supervised learning, it comes up with a third solution for concept variant problem in game props recommendation task, i.e., Co-MIML with co-training techniques.
Semi-supervised feature selection methods are thus necessary, and many solutions have been proposed recently.
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