Boosting Classifiers for Drifting Concepts

This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the data and are thus not suited for mining massive streams. --

Bestimmung von Isothermenparametern mit Hilfe des maschinellen Lernens

Die Modellierung eines chromatographischen Prozesses ermöglicht die Bestimmung der optimalen Betriebsparameter für die Trennung der Komponenten eines Stoffgemisches mittels modellbasierter Optimierung.Voraussetzung für eine genaue Modellierung ist dabei die Kenntnis der stoffabhängigen Parameter des zugrunde liegenden physikalischen Modells,insbesondere der Adsorptionsisotherme. Die esstechnische Bestimmung der Isotherme ist zeit-und materialaufwändig und deshalb nicht generell durchführbar.Eine Bestimmung der Isothermenparameter aus Chromatogrammen mittels mathematischer Parameterschätzung hingegen senkt diesen Aufwand,hat jedoch den Nachteil der Abhängigkeit von guten Startwerten.Die hier vorgestellte Methode nutzt die Approximationsfähigkeiten von Support Vector Machines zur Bestimmung von Isothermenparametern aus wenigen Merkmalen von Chromatogrammen und erfordert derartige Startwerte nicht

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

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1996). Appendices A to F have been added for purposes normal to thesis writing. iii Symbolic inductive learning systems that induce concept descriptions from examples are valuable tools in the task of knowledge acquisition for expert systems. Since inductive learning methods produce distinct concept descriptions when given identical training data, questions arise as to the quality of the different rule sets produced. This work provides several techniques for comparing and analyzing rule sets. These techniques measure the accuracy, generalization, time and space complexity, and domain coverage of rule sets. Based on these metrics, the performance of four different inductive learning systems is compared. These systems are Michalski et al.’s AQ15 (1986a; 1986b; Hong, Mozetic, and Michalski, 1986; Wnek et al., 1995), Quinlan’s C4.5 (1993), Clark and Niblett’s CN2 (Clark and Niblett

Adaptive information filtering: Learning in the presence of concept drifts

The task of information ltering is to classify texts from a stream of documents into relevant and nonrelevant, respectively, with respect to a particular category or user interest, which maychange over time. A ltering system should be able to adapt to such concept changes. This paper explores methods to recognize concept changes and to maintain windows on the training data, whose size is either xed or automatically adapted to the current extent of concept change. Experiments with two simulated concept drift scenarios based on real-world text data and eight learning methods are performed to evaluate three indicators for concept changes and to compare approaches with xed and adjustable window sizes, respectively, to each other and to learning on all previously seen examples. Even using only a simple window on the data already improves the performance of the classi ers signi cantly as compared to learning on all examples. For most of the classi ers, the window adjustments lead to a further increase in performance compared to windows of xed size. The chosen indicators allow to reliably recognize concept changes

Learning Drifting Concepts with Partial User Feedback, Beiträge zum Treffen der GIFachgruppe

Abstract. The task of information filtering is to classify texts from a stream of documents into relevant and irrelevant, respectively, with respect to a particular category or user interest, which may change over time. A filtering system should be able to adapt to such concept changes and to cope the problem of users giving only partial feedback. This paper explores methods to recognize concept changes and to maintain windows on the training data, whose size is either fixed or automatically adapted to the current extent of concept change. Experiments with two simulated concept drift scenarios based on real-world text data and four learning methods are performed to evaluate three indicators for concept changes and to compare approaches with fixed and adjustable window sizes, respectively, to each other and to learning on all previously seen examples. Additional experiments test the adaptive window size approach with four simulated user behaviours with partial feedback in the two aforementioned scenarios. Even using only a simple window on the data already improves the performance of the classifiers significantly as compared to learning on all examples. For most of the classifiers, the window adjustments lead to a further increase in performance compared to windows of fixed size. The chosen indicators allow to reliably recognize concept changes, even if only partial user feedback is available