Prof. Myra Spiliopoulou,
Knowledge Discovery in mHealth — dealing with few noisy data
Patients with chronic diseases can greatly benefit from mHealth technology. There are solutions assisting them in measuring signals (eg blood pressure, sugar level), in keeping a diary with Ecological Momentary Assessments(EMA), such as physical exercise, onset of symptoms and subjective perception of health condition. Machine learning can deliver useful insights from data thus collected. While sensor signals can be collected without interruption, EMA recording depends on patients’ self-discipline and compliance.
The talk starts with an overview of the role of mHealth applications in diagnostics and treatment support. Then, we focus on EMA for chronic conditions. We discuss challenges of learning from few and noisy recordings, and methods for prediction and risk factor identification on these data.
MYRA SPILIOPOULOU is Professor of Business Information Systems at the Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany. Her main research is on mining dynamic complex data. Her publications are on mining complex streams, mining evolving objects, adapting models to drift and building models that capture drift. She focusses on two application areas: (a) business, including opinion stream mining and adaptive recommenders, and (b) medical research, including epidemiological mining and learning from clinical studies. In the application domain of medical research, she works on modeling and predicting evolution of study participants with and without the target outcome. In the area of medical research, she is currently involved in the CHRODIS+ (2017-2020) EU Joint Action on “Implementing good practices for chronic diseases” and in the UNITI (2020-2022) EU Project on “Unification of treatments and Interventions for Tinnitus patients”.
Her research on topic monitoring, social network monitoring and analysis of complex dynamic data has been published in renowned international conferences and journals. She is regularly presenting tutorials on different aspects of complex data mining, and recently on medical mining, including a tutorial on medical mining at KDD 2019. She is involved as (senior) reviewer in major conferences on data mining and knowledge discovery. In 2018, she was a PC Chair of the Applied Data Science Track in the ACM SIGKDD Int. Conf. on Knowledge Discovery from Data (KDD’2018), London, Aug. 2018. In 2016 and 2019, she served as a PC Chair of the IEEE Int. Symposium on Computer-Based Medical Systems (CBMS). In 2020, she serves as a Chair for Tutorials and Workshops at ECML PKDD 2020.
She is member of the Presidium of the European Association of Data Science (EuADS). In Germany, she is member of the Jury for the best PhD Award of the German Informatics Society. Since April 2016, she serves as Action Editor for the Data Mining and Knowledge Discovery Journal of Springer (DAMI). Within KDD 2019, she served as Jury member for the best KDD dissertation award.
Prof. Peter A. Flach,
University of Bristol
The highs and lows of performance evaluation: Towards a measurement theory for machine learning
Our understanding of performance evaluation measures for machine-learned classifiers has improved considerably over the last decades. However, there is a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This is clearly problematic, since if machine learning researchers are unclear about what exactly their experiments are telling them about their machine learning algorithms, then how can end-users trust systems deploying those algorithms?
I suggest that in order to make further progress we need to develop a proper measurement theory of machine learning. Measurement theory studies the concepts of measurement and scale. If you have a way to measure, say, the length of individual rods or planks, this should also allow you to then calculate the combined length of concatenated rods or planks. What relevant concatenation operations are there in data science and AI, and what does that mean for the underlying measurement scale?
I discuss by example what such a measurement theory might look like and what kinds of new results it would entail. I furthermore argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models. Ultimately, machine learning experiments need to go beyond simple correlations and aim to make causal inferences of the form ‘Algorithm A outperformed algorithm B because to classes were highly imbalanced’, or counterfactually, ‘if the classes were re-balanced, the observed performance difference between A and B would disappear’.
Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. An internationally leading researcher in the areas of mining highly structured data and the evaluation and improvement of machine learning models using ROC analysis, he has also published on the logic and philosophy of machine learning, and on the combination of logic and probability. He is author of Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012).
Prof Flach is the Editor-in-Chief of the Machine Learning journal, one of the two top journals in the field that has been published for over 25 years by Kluwer and now Springer. He was Programme Co-Chair of the 1999 International Conference on Inductive Logic Programming, the 2001 European Conference on Machine Learning, the 2009 ACM Conference on Knowledge Discovery and Data Mining, and the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases in Bristol.
Prof. Gustau Camps-Valls,
Universitat de València
Machine learning for Modelling and Understanding in Earth Sciences
The Earth is a complex dynamic network system. Modelling and understanding the system is at the core of scientific endeavour. We approach these problems with machine learning algorithms. I will review several ML approaches we have developed in the last years: 1) advanced Gaussian processes models for bio-geo-physical parameter estimation, which can incorporate physical laws, blend multisensor data while providing credible confidence intervals for the estimates and improved interpretability, 2) nonlinear dimensionality reduction methods to decompose Earth data cubes in spatially-explicit and temporally-resolved modes of variability that summarize the information content of the data and allow for identifying relations with physical processes, and 3) advances in causal inference that can uncover cause and effect relations from purely observational data.
Gustau Camps-Valls (M’04–SM’07–F’18) received the Ph.D. degree in physics from the Universitat de València, València, Spain, in 2002.,He is currently a Full Professor of electrical engineering and a Coordinator of the Image and Signal Processing Group, Image Processing Laboratory, Universitat de València. He is also involved in the development of machine learning algorithms for geoscience and remote sensing data analysis. He has authored 200 journal papers, more than 200 conference papers, and 20 international book chapters. He holds Hirsch’s index, h = 60 (source: Google Scholar), entered the ISI list of Highly Cited Researchers in 2011, and Thomson Reuters ScienceWatch identified one of his papers on kernel-based analysis of hyperspectral images as a Fast Moving Front research.,Dr. Camps-Valls was a recipient of the prestigious European Research Council (ERC) Consolidator Grant on Statistical Learning for Earth Observation Data Analysis in 2015. He has been an Associate Editor of the IEEE Transactions on Signal Processing, IEEE Geoscience and Remote Sensing Letters, and IEEE Signal Processing Letters. He was the Invited Guest Editor of the IEEE Journal of Selected Topics in Signal Processing in 2012 and IEEE Geoscience and Remote Sensing Magazine in 2015. He also serves as an Editor for the books: Kernel Methods Engineering, Signal and Image Processing (IGI, 2007), Kernel Methods for Remote Sensing Data Analysis (Wiley & Sons, 2009), Remote Sensing Image Processing (MC, 2011), and Digital Signal Processing With Kernel Methods (Wiley & Sons, 2018).