This article is the first one to examine the CC algorithm for protein function prediction using semi-supervised learning and multi-label learning techniques to leverage the unlabeled portion of the data and label correlation information in the partially-labeled PPI network, which only has limited number of annotations.
A KNIME workflow [14] was created in order to collect data from the various sources, preprocess the data, merge it via InChIKeys [23], and filter it for the generation of the datasets of different sizes to be used by the various multi-label learning techniques.
Multi-label learning and imbalance learning techniques are necessary to process the medical data sets with above problems.
Further work may focus on novel combination of multi-label learning and imbalance learning techniques to improve the accuracy of classification.
Because of the impact of different uncertainties together with the lack of labeled training samples, the conventional machine learning techniques face complicated challenges to develop reliable classification models.
Finally, it is possible to automatically learn the appropriate parameter values through machine learning techniques, provided that proper labeled training sets are available.
Supervised machine learning techniques are used, based on a training set of labelled samples, to build models that are able to automatically label previously unclassified samples [ 28, 29].
Driverless cars are already using machine learning techniques, while deep learning models have the ability to label pictures and films with rich, detailed descriptions about their content.
This makes it different from other machine learning techniques — like Supervised Learning, for example — as it doesn't require any labeled training data to get started, and it can make short-term decisions while optimizing for a long-term goal.
First, a lot of new machine learning techniques have been proposed to solve the sequence labelling problem successfully.
In machine learning techniques, patterns for identifying relations are not manually written (handcrafted rules) but are learned from labeled examples [71].
