## 3 The Proposed Co-Training Scheme
Otherwise, application to real-world data from totally different domains might infer biased decisions regarding the applicabilityto a wider range of datasets. Transfer learning, combined probably with Active Learning framework that permits the knowledge blending of human factors into the learning pipeline, could prove to be a valuable approach for exporting more robust classifiers by enriching the feature vector and/or applying more compatible modifications [73]. Finally, deep neural networks (DNNs) [74] could be employed under a co-training scheme to boost the predictive performance, fed with either raw data or other generic kinds of datasets. More specifically, long short term memory (LSTM) networks have already proven efficient enough when combined with SSL methods for constructing clinical support decision systems [75]. In case DNNs should be exploited, creation of new insights into inserted data could take place, providing either totally new view (s) or augmenting the existing one (s). Thus, several feature engineering approaches should be adopted to enhance the quality of the co-training scheme and possibly violate the assumption about the independent views less. Conceptualization, S.K. ([PERSON]); methodology, S.K. ([PERSON]), G.K. and S.K. ([PERSON]); software, S.K. ([PERSON]); validation, S.K. ([PERSON]), G.K. and S.K. ([PERSON]); formal analysis, G.K. and S.K. ([PERSON]); investigation, S.K. ([PERSON]); resources, S.K. ([PERSON]), G.K. and S.K. ([PERSON]); data curation, S.K. ([PERSON]); writing--original draft preparation, S.K. ([PERSON]), G.K.; writing--review and editing, S.K. ([PERSON]), G.K. and S.K. ([PERSON]); visualization, S.K. ([PERSON]); supervision, S.K. ([PERSON]); project administration, S.K. ([PERSON]); funding acquisition, S.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. ## References

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