Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI

Abstract

In this short editorial we present some thoughts on present and future trends in Artificial Intelligence (AI) generally, and Machine Learning (ML) specifically. Due to the huge ongoing success in machine learning, particularly in statistical learning from big data, there is rising interest of academia, industry and the public in this field. Industry is investing heavily in AI, and spin-offs and start-ups are emerging on an unprecedented rate. The European Union is allocating a lot of additional funding into AI research grants, and various institutions are calling for a joint European AI research institute. Even universities are taking AI/ML into their curricula and strategic plans. Finally, even the people on the street talk about it, and if grandma knows what her grandson is doing in his new start-up, then the time is ripe: We are reaching a new AI spring. However, as fantastic current approaches seem to be, there are still huge problems to be solved: the best performing models lack transparency, hence are considered to be black boxes. The general and worldwide trends in privacy, data protection, safety and security make such black box solutions difficult to use in practice. Specifically in Europe, where the new General Data Protection Regulation (GDPR) came into effect on May, 28, 2018 which affects everybody (right of explanation). Consequently, a previous niche field for many years, explainable AI, explodes in importance. For the future, we envision a fruitful marriage between classic logical approaches (ontologies) with statistical approaches which may lead to context-adaptive systems (stochastic ontologies) that might work similar as the human brain.

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

“…Emotions play a central role in explainable artificial intelligence (AI), where there is so much need for human-AI interaction and human-AI interfaces [73]. As future research directions, we intend to classify the six basic emotions into three classes, namely negative, neutral, and positive and to develop emotion-based applications starting from the results presented in this paper, in the emerging field of explainable AI.…”

Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error

“…Data science is currently receiving much attention across various fields because of the big data-wave which is flooding all areas of science and our society [79][80][81][82][83]. Model selection and model assessment are two important concepts when studying statistical inference, and every data scientist needs to be familiar with this in order to select the best model and to assess its prediction capabilities fairly in terms of the generalization error.…”

Decision Support Software for Forecasting Patient’s Length of Stay

“…Additionally, to address the problem of imbalanced dataset and further increase the accuracy of the proposed two-level classifier, we commit to incorporate techniques dedicated for imbalanced data such as feature selection, sampling, cost-sensitive learning, and instance weighting [35][36][37][38][39][40]. Furthermore, an interesting aspect of future research is to update the presented software to provide on demand explanation of what are the underlying explanatory factors for reaching a certain prediction/decision [41]. The development of a contextual explanatory model could further assist medical staff in the decision-making process.…”