Prof. Dr. Stefan Lessmann

Profil

• EXIST Creatext

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  Förderer: BMWE: EXIST Zeitraum: 11/2019 - 10/2020 Projektleitung: Prof. Dr. Stefan Lessmann

• EX: Sentientic

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  Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 11/2015 - 10/2016 Projektleitung: Prof. Dr. Stefan Lessmann

• GRK 1792: Hochdimensionale nicht stationäre Zeitreihen

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  Förderer: DFG Graduiertenkolleg Zeitraum: 01/2013 - 06/2023 Projektleitung: Prof. Dr. Wolfgang Karl Härdle

• IGRK 1792/2: Hochdimensionale nicht stationäre Zeitreihen

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  Förderer: DFG Graduiertenkolleg Zeitraum: 04/2018 - 06/2023 Projektleitung: Prof. Dr. Wolfgang Härdle

• KI im Kundenservice (KIK)

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  Förderer: Investitionsbank Berlin (IBB) Zeitraum: 02/2025 - 10/2027 Projektleitung: Prof. Dr. Alan Akbik, Prof. Dr. Stefan Lessmann

• Missing Link

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  Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 05/2018 - 04/2019 Projektleitung: Prof. Dr. Stefan Lessmann

• Operations Research in der Wirtschaftsinformatik

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  Förderer: Andere Hochschulfördergesellschaften Zeitraum: 06/2016 - 12/2020 Projektleitung: Prof. Dr. Stefan Lessmann

• recreate goods

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  Förderer: BMWE: EXIST Zeitraum: 06/2025 - 06/2026 Projektleitung: Prof. Dr. Stefan Lessmann

• OMQ GmbH

  KPT

  18 Treffer85.0%
  • KI im Kundenservice (KIK)
    K

    85.0%

• Science & Startups Berlin

  T

  8 Treffer60.7%
  • Zuwendung im Rahmen des Programms „exist – Existenzgründungen aus der Wissenschaft“ aus dem Bundeshaushalt, Einzelplan 09, Kapitel 02, Titel 68607, Haushaltsjahr 2026, sowie aus Mitteln des Europäischen Strukturfonds (hier Euro-päischer Sozialfonds Plus – ESF Plus) Förderperiode 2021-2027 – Kofinanzierung für das Vorhaben: „exist Women“
    T

    60.7%

• Process Systems Enterprise Limited

  P

  51 Treffer60.5%
  • Systematic Models for Biological Systems Engineering Training Network
    P

    60.5%

• Insilico Biotechnology AG

  P

  52 Treffer60.5%
  • Systematic Models for Biological Systems Engineering Training Network
    P

    60.5%

• Silicolife LDA

  P

  50 Treffer60.5%
  • Systematic Models for Biological Systems Engineering Training Network
    P

    60.5%

• Protatuans-Etaireia Ereynas Viotechologias Monoprosopi Etaireia Periorisments Eythinis

  P

  44 Treffer60.5%
  • Systematic Models for Biological Systems Engineering Training Network
    P

    60.5%

• DSM Food Specialities BV

  P

  22 Treffer60.3%
  • Engineering of New-Generation Protein Secretion Systems
    P

    60.3%

• UCB Celltech

  P

  21 Treffer60.3%
  • Engineering of New-Generation Protein Secretion Systems
    P

    60.3%

• novozymess

  P

  20 Treffer60.3%
  • Engineering of New-Generation Protein Secretion Systems
    P

    60.3%

• Gesellschaft für angewandte Mathematik und Mechanik

  PT

  129 Treffer59.3%
  • Workshop Reliable Methods and Mathematical Modeling
    P

    59.3%

• Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

  2008

  IEEE Transactions on Software Engineering · 1211 Zitationen · DOI

  Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.

• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

  2015

  European Journal of Operational Research · 1098 Zitationen · DOI

• A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data

  2018

  Solar Energy · 317 Zitationen · DOI

• Deep learning for detecting financial statement fraud

  2020

  Decision Support Systems · 263 Zitationen · DOI

• The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

  2005

  European Journal of Operational Research · 215 Zitationen · DOI

• A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry

  2016

  Decision Support Systems · 201 Zitationen · DOI

• Bridging the divide in financial market forecasting: machine learners vs. financial economists

  2016

  Expert Systems with Applications · 189 Zitationen · DOI

• Predicting online shopping behaviour from clickstream data using deep learning

  2020

  Expert Systems with Applications · 161 Zitationen · DOI

  • We examine deep learning based conversion modeling in digital marketing. • We use LSTM and GRU for clickstream classification to predict e-coupon redemption. • We find high forecast diversity between RNNs and conventional classifiers. • An ensemble of GRU and GBM predicts coupon redemption with high accuracy. Clickstream data is an important source to enhance user experience and pursue business objectives in e-commerce. The paper uses clickstream data to predict online shopping behavior and target marketing interventions in real-time. Such AI-driven targeting has proven to save huge amounts of marketing costs and raise shop revenue. Previous user behavior prediction models rely on supervised machine learning (SML). Conceptually, SML is less suitable because it cannot account for the sequential structure of clickstream data. The paper proposes a methodology capable of unlocking the full potential of clickstream data using the framework of recurrent neural networks (RNNs). An empirical evaluation based on real-world e-commerce data systematically assesses multiple RNN classifiers and compares them to SML benchmarks. To this end, the paper proposes an approach to measure the revenue impact of a targeting model. Estimates of revenue impact together with results of standard classifier performance metrics evidence the viability of RNN-based clickstream modeling and guide employing deep recurrent learners for campaign targeting. Given that the empirical analysis shows RNN-based and conventional classifiers to capture different patterns in clickstream data, a specific recommendation is to combine sequence and conventional classifiers in an ensemble. The paper shows such an ensemble to consistently outperform the alternative models considered in the study.

• Extreme learning machines for credit scoring: An empirical evaluation

  2017

  Expert Systems with Applications · 147 Zitationen · DOI

• Fairness in credit scoring: Assessment, implementation and profit implications

  2021

  European Journal of Operational Research · 145 Zitationen · DOI

• Incorporating textual information in customer churn prediction models based on a convolutional neural network

  2019

  International Journal of Forecasting · 140 Zitationen · DOI

• A multi-objective approach for profit-driven feature selection in credit scoring

  2019

  Decision Support Systems · 135 Zitationen · DOI

• Genetic Algorithms for Support Vector Machine Model Selection

  2006

  The 2006 IEEE International Joint Conference on Neural Network Proceedings · 122 Zitationen · DOI

  The support vector machine is a powerful classifier that has been successfully applied to a broad range of pattern recognition problems in various domains, e.g. corporate decision making, text and image recognition or medical diagnosis. Support vector machines belong to the group of semiparametric classifiers. The selection of appropriate parameters, formally known as model selection, is crucial to obtain accurate classification results for a given task. Striving to automate model selection for support vector machines we apply a meta-strategy utilizing genetic algorithms to learn combined kernels in a data-driven manner and to determine all free kernel parameters. The model selection criterion is incorporated into a fitness function guiding the evolutionary process of classifier construction. We consider two types of criteria consisting of empirical estimators or theoretical bounds for the generalization error. We evaluate their effectiveness in an empirical study on four well known benchmark data sets to find that both are applicable fitness measures for constructing accurate classifiers and conducting model selection. However, model selection focuses on finding one best classifier while genetic algorithms are based on the idea of re-combining and mutating a large number of good candidate classifiers to realize further improvements. It is shown that the empirical estimator is the superior fitness criterion in this sense, leading to a greater number of promising models on average.

• Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting

  2019

  European Journal of Operational Research · 115 Zitationen · DOI

• Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germany

  2019

  Tourism Management · 110 Zitationen · DOI

• Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda

  2023

  European Journal of Operational Research · 109 Zitationen · DOI

  The ability to understand and explain the outcomes of data analysis methods, with regard to aiding decision-making, has become a critical requirement for many applications. For example, in operational research domains, data analytics have long been promoted as a way to enhance decision-making. This study proposes a comprehensive, normative framework to define explainable artificial intelligence (XAI) for operational research (XAIOR) as a reconciliation of three subdimensions that constitute its requirements: performance, attributable, and responsible analytics. In turn, this article offers in-depth overviews of how XAIOR can be deployed through various methods with respect to distinct domains and applications. Finally, an agenda for future XAIOR research is defined.

• A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters

  2021

  Information Systems Frontiers · 102 Zitationen · DOI

• Improving crime count forecasts using Twitter and taxi data

  2018

  Decision Support Systems · 89 Zitationen · DOI

• Forex exchange rate forecasting using deep recurrent neural networks

  2020

  Digital Finance · 87 Zitationen · DOI

  Abstract Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures. Especially with regard to trading profit, a simpler neural network may perform as well as if not better than a more complex deep neural network.

• A reference model for customer-centric data mining with support vector machines

  2008

  European Journal of Operational Research · 84 Zitationen · DOI

• Targeting customers for profit: An ensemble learning framework to support marketing decision-making

  2019

  Information Sciences · 73 Zitationen · DOI

• A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

  2019

  Information Systems and e-Business Management · 71 Zitationen · DOI

• Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy

  2017

  International Journal of Forecasting · 70 Zitationen · DOI

• Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach

  2020

  European Journal of Operational Research · 67 Zitationen · DOI

• Antisocial online behavior detection using deep learning

  2020

  Decision Support Systems · 61 Zitationen · DOI

• Freie Universität Berlin

  GRK 1792: Hochdimensionale nicht stationäre Zeitreihen

  university

• OMQ GmbH

  KI im Kundenservice (KIK)

  company

• Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin

  GRK 1792: Hochdimensionale nicht stationäre Zeitreihen

  other

• Xiamen Universität

  IGRK 1792/2: Hochdimensionale nicht stationäre Zeitreihen

  university

Name

Prof. Dr. Stefan Lessmann

Titel

Prof. Dr.

Fakultät

Wirtschaftswissenschaftliche Fakultät

Institut

Wirtschaftsinformatik

Telefon

+49 30 2093-99542

HU-FIS-Profil

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Zuletzt gescrapt

26.4.2026, 01:08:32