Professor Jamal Ouenniche Event details Venue: Business School Concourse 17:30- Lecture start 18:20 - Q&A 18:45 - Drinks Reception. - A small drinks reception will follow the lecture. All guest are welcome to attend. Lecture abstract Nowadays, the fields of data analytics, in general, and predictive analytics, in particular, are witnessing an unprecedented interest from a variety of stakeholders across many industries. This talk is concerned with a new class of high-performance classifiers to predict risk class belonging and proposes a generic classification framework with plug-and-play implementation options. The empirical performance of the proposed classifiers is tested on a data set of UK firms listed on the London Stock Exchange in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to implementation decisions’ choices. This exceptional predictive performance of the proposed new classifiers makes them real contenders in actual applications in areas such as finance and investment, internet security, fraud, medical diagnosis, and profiling of criminals to name a few, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders. Nov 20 2019 17.30 - 19.30 Professor Jamal Ouenniche Details of Professor Jamal Ouenniche's inaugural lecture Business School Concourse, 29 Buccleuch Place, Edinburgh
Professor Jamal Ouenniche Event details Venue: Business School Concourse 17:30- Lecture start 18:20 - Q&A 18:45 - Drinks Reception. - A small drinks reception will follow the lecture. All guest are welcome to attend. Lecture abstract Nowadays, the fields of data analytics, in general, and predictive analytics, in particular, are witnessing an unprecedented interest from a variety of stakeholders across many industries. This talk is concerned with a new class of high-performance classifiers to predict risk class belonging and proposes a generic classification framework with plug-and-play implementation options. The empirical performance of the proposed classifiers is tested on a data set of UK firms listed on the London Stock Exchange in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to implementation decisions’ choices. This exceptional predictive performance of the proposed new classifiers makes them real contenders in actual applications in areas such as finance and investment, internet security, fraud, medical diagnosis, and profiling of criminals to name a few, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders. Nov 20 2019 17.30 - 19.30 Professor Jamal Ouenniche Details of Professor Jamal Ouenniche's inaugural lecture Business School Concourse, 29 Buccleuch Place, Edinburgh
Nov 20 2019 17.30 - 19.30 Professor Jamal Ouenniche Details of Professor Jamal Ouenniche's inaugural lecture
