Ranking for semi-supervised learning

Parul Manchda

Abstract


High dimensionality, the quantity of instances, and the availability of labels for the examples all contribute to the complexity of the analysis's data. The analysis of datasets with numerous instances given in a high-dimensional space, but not all examples have labels supplied, presents a number of obstacles for the current machine learning techniques. For instance, there are several chemical compounds that are accessible and that can be characterised using information-rich high-dimensional representations, but not all of the compounds include information about their toxicity. We provide strategies for feature rankings semi-supervised learning (SSL) to overcome these issues.

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