Algorithmic Techniques for Processing Data Streams

We give a survey at some algorithmic techniques for processing data streams. After covering the basic methods of sampling and sketching, we present more evolved procedures that resort on those basic ones. In particular, we examine algorithmic schemes for similarity mining, the concept of group testing, and techniques for clustering and summarizing data streams

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201

Профилакса на Hepatitis B кај деца родени од HBsAg позитивни мајки во Mедицински центар во Штип

Цел на трудот: Профилакса на вертикална трансмисија на Hepatitis B. Материјал и методи: Децата родени од HBsAg позитивни мајки веднаш по породувањето се тестирани за утврдување присуство на HBsAg со Микроелиса тест од трета генерација (Organon Teknika). Како потврден тест е користен Lia Tek HCV (Organon Teknika). Наредните тестирања се извршени пред апликација на втора доза HB вакцина, потоа 1 месец по добивање на втора профилактична доза, потоа пред апликација на третата и 1, 6 и 12 месеци по третата профилактична доза. Се детектираше и присуство на анти-HBs (Nubenco Diagnostics, Inc. One Kalisa Way, Paramus, (U.S.A.). Резултати: од 1050 редовно тестирани бремени жени присуство на HBsAg е откриено кај 14(1,33%). Кај ниту една од испитуваните бремени ѓени не е регистриран акутен хепатит, ниту пореметување на хепаталниот и ензимскиот статуст. Веднаш по породувањето сите 14 новородени деца се тестирани за присуство на HBsAg. Кај ниту едно не е откриено присуство на HBsAg..Поради тоа аплицирани се i.m. 0,5 ml ) ENGERIX генетички произведена HB вакцина. Серум (HBIG) не аплициравме поради немање можност од набавка. Еден месец по апликацијата на втората доза HB вакцина, потоа 1, 6 и 12 месеци од апликацијата на третата доза HB вакцина, серумски примероци од новородените деца се тестирани за откривање присуство на HBsAg I anti - HBs.. Testirawata poka\aa negativni rezultati. Kaj ни едно од испитаните деца не е регисран акутен хепатит, ниту пореметување на хепаталниот и ензимскиот статус.. И покрај забраната за доење дел од децата се доени без наша согласност. Заклучок: И покрај тоа што нашите резултати се разликуваат од многу податоци во литературата со иста или слична цел, сепак препорачуваме профилактичен третман на сите новородени деца од HBsAg позитивни мајки со HBIG I HB вакцина, и забрана за доење

Interval forecasts based on regression trees for streaming data

In forecasting, we often require interval forecasts instead of just a specific point forecast. To track streaming data effectively, this interval forecast should reliably cover the observed data and yet be as narrow as possible. To achieve this, we propose two methods based on regression trees: one ensemble method and one method based on a single tree. For the ensemble method, we use weighted results from the most recent models, and for the single-tree method, we retain one model until it becomes necessary to train a new model. We propose a novel method to update the interval forecast adaptively using root mean square prediction errors calculated from the latest data batch. We use wavelet-transformed data to capture long time variable information and conditional inference trees for the underlying regression tree model. Results show that both methods perform well, having good coverage without the intervals being excessively wide. When the underlying data generation mechanism changes, their performance is initially affected but can recover relatively quickly as time proceeds. The method based on a single tree performs the best in computational (CPU) time compared to the ensemble method. When compared to ARIMA and GARCH modelling, our methods achieve better or similar coverage and width but require considerably less CPU time

A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning

Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniques, which are much faster than FEM. This paper assesses the feasibility of ML methods for modelling the biomechanical behaviour of the human liver during the breathing process, which is crucial for guiding surgeons during interventions where it is critical to track this deformation (e.g. some specific kind of biopsies) or for the accurate application of radiotherapy dose to liver tumours. For this purpose, different ML regression models were investigated, including three tree-based methods (decision trees, random forests and extremely randomised trees) and other two simpler regression techniques (dummy model and linear regression). In order to build and validate the ML models, a labelled data set was constructed from modelling the deformation of eight ex-vivo human livers using FEM. The best prediction performance was obtained using extremely randomised trees, with a mean error of 0.07 mm and all the samples with an error under 1 mm. The achieved results lay the foundation for the future development of some real-time software capable of simulating the human liver deformation during the breathing process during clinical interventions.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R and DPI2013-40859-R, both also supported by European FEDER funds. The authors acknowledge the kind collaboration of the personnel from the hospital involved in the research.Lorente, D.; Martínez-Martínez, F.; Rupérez Moreno, MJ.; Lago, MA.; Martínez-Sober, M.; Escandell-Montero, P.; Martínez-Martínez, JM.... (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications. 71:342-357. doi:10.1016/j.eswa.2016.11.037S3423577

Automated Adaptation Strategies for Stream Learning

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism