Geometric Heuristics for Transfer Learning in Decision Trees

Motivated by a network fault detection problem, we study how recall can be boosted in a decision tree classifier, without sacrificing too much precision. This problem is relevant and novel in the context of transfer learning (TL), in which few target domain training samples are available. We define a geometric optimization problem for boosting the recall of a decision tree classifier, and show it is NP-hard. To solve it efficiently, we propose several near-linear time heuristics, and experimentally validate these heuristics in the context of TL. Our evaluation includes 7 public datasets, as well as 6 network fault datasets, and we compare our heuristics with several existing TL algorithms, as well as exact mixed integer linear programming (MILP) solutions to our optimization problem. We find that our heuristics boost recall in a manner similar to optimal MILP solutions, yet require several orders of magnitude less compute time. In many cases th

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

Feedback Admission Control for Workflow Management Systems

We propose a novel feedback admission control (FAC) algorithm based on control theory as a unified framework to improve the real-time scheduling (RTS) performance in industrial workflow management systems (WMSs). Our FAC algorithm is based on four main principles. First, it does not require the knowledge of RTS parameters of jobs prior to their arrival to the system for scheduling and processing. Second, it does not require a change of the scheduling architecture/policy in the industrial WMS which is a requirement in some industries including the one under consideration in this thesis. Third, we derive dynamic models for computing systems for the purpose of performance control. Finally, we apply established control laws to manage the trade-offs in meeting deadlines and increasing platform utilisation (classical RTS objectives). The generality and efficiency of our proposed FAC algorithm are demonstrated by its application in three typical scheduling scenarios in industry. First, we tested our algorithm with simple tasks that are periodic and independent. For this application, we developed two FAC versions based on basic and advanced control laws to compare their performance with respect to the RTS objectives. Second, we added task dependencies as a scheduling constraint because they are witnessed in some industrial workloads. We evaluated our FAC algorithm against other baseline algorithms like the completion-ratio admission controller with respect to the RTS objectives. Third, we extended our FAC algorithm to support enterprise resource planning decisions in acquiring additional computing processors in real-time to further achieve the RTS objectives while constrained by industrial projects’ financial budgets