234 research outputs found

    An Overview of Classifier Fusion Methods

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    A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

    An Overview of Classifier Fusion Methods

    Get PDF
    A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

    Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

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    As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms. This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral fusion improves creditworthiness evaluations by synthesizing the prediction results of multiple classifiers and finding the largest consistency between outcomes among conflicting and consistent results. The proposed model was validated through experimental analysis on a real- world dataset from a well-known P2P lending marketplace. The empirical results indicate that the combination of multiple classifiers based on fuzzy Choquet integrals outperforms the best base classifiers used in credit risk prediction to date. In addition, the proposed methodology is superior to some conventional combination techniques

    A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

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    © 2019 IEEE. Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers

    Parameter identification in Choquet Integral by the Kullback-Leibler diversgence on continuous densities with application to classification fusion.

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    International audienceClassifier fusion is a means to increase accuracy and decision-making of classification systems by designing a set of basis classifiers and then combining their outputs. The combination is made up by non linear functional dependent on fuzzy measures called Choquet integral. It constitues a vast family of aggregation operators including minimum, maximum or weighted sum. The main issue before applying the Choquet integral is to identify the 2M − 2 parameters for M classifiers. We follow a previous work by Kojadinovic and one of the authors where the identification is performed using an informationtheoritic approach. The underlying probability densities are made smooth by fitting continuous parametric and then the Kullback-Leibler divergence is used to identify fuzzy measures. The proposed framework is applied on widely used datasets

    Learning nonlinear monotone classifiers using the Choquet Integral

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    In der jĂŒngeren Vergangenheit hat das Lernen von Vorhersagemodellen, die eine monotone Beziehung zwischen Ein- und Ausgabevariablen garantieren, wachsende Aufmerksamkeit im Bereich des maschinellen Lernens erlangt. Besonders fĂŒr flexible nichtlineare Modelle stellt die GewĂ€hrleistung der Monotonie eine große Herausforderung fĂŒr die Umsetzung dar. Die vorgelegte Arbeit nutzt das Choquet Integral als mathematische Grundlage fĂŒr die Entwicklung neuer Modelle fĂŒr nichtlineare Klassifikationsaufgaben. Neben den bekannten Einsatzgebieten des Choquet-Integrals als flexible Aggregationsfunktion in multi-kriteriellen Entscheidungsverfahren, findet der Formalismus damit Eingang als wichtiges Werkzeug fĂŒr Modelle des maschinellen Lernens. Neben dem Vorteil, Monotonie und FlexibilitĂ€t auf elegante Weise mathematisch vereinbar zu machen, bietet das Choquet-Integral Möglichkeiten zur Quantifizierung von Wechselwirkungen zwischen Gruppen von Attributen der Eingabedaten, wodurch interpretierbare Modelle gewonnen werden können. In der Arbeit werden konkrete Methoden fĂŒr das Lernen mit dem Choquet Integral entwickelt, welche zwei unterschiedliche AnsĂ€tze nutzen, die Maximum-Likelihood-SchĂ€tzung und die strukturelle Risikominimierung. WĂ€hrend der erste Ansatz zu einer Verallgemeinerung der logistischen Regression fĂŒhrt, wird der zweite mit Hilfe von Support-Vektor-Maschinen realisiert. In beiden FĂ€llen wird das Lernproblem imWesentlichen auf die Parameter-Identifikation von Fuzzy-Maßen fĂŒr das Choquet Integral zurĂŒckgefĂŒhrt. Die exponentielle Anzahl von Freiheitsgraden zur Modellierung aller Attribut-Teilmengen stellt dabei besondere Herausforderungen im Hinblick auf LaufzeitkomplexitĂ€t und Generalisierungsleistung. Vor deren Hintergrund werden die beiden AnsĂ€tze praktisch bewertet und auch theoretisch analysiert. Zudem werden auch geeignete Verfahren zur KomplexitĂ€tsreduktion und Modellregularisierung vorgeschlagen und untersucht. Die experimentellen Ergebnisse sind auch fĂŒr anspruchsvolle Referenzprobleme im Vergleich mit aktuellen Verfahren sehr gut und heben die NĂŒtzlichkeit der Kombination aus Monotonie und FlexibilitĂ€t des Choquet Integrals in verschiedenen AnsĂ€tzen des maschinellen Lernens hervor
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