ELM Clustering – Application to Bankruptcy Prediction

Anton Akusok, David Veganzones, Kaj-Mikael Björk, Eric Séverin, Philippe du Jardin, Amaury Lendasse, Yoan Miche

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

This paper presents a new clustering technique based on Ex-treme Learning Machine (ELM). This clustering technique can incorpo-rate a priori knowledge (of an expert) to define the optimal structure of the clustering; for example, the number of points in each cluster. Using ELM, the clustering can be rewritten as a Traveling Salesman Problem and solved by a Genetic Algorithm. This efficient and fast clustering technique is used in order to analyze and predict financial distress of French companies.
Original languageEnglish
Title of host publicationInternational work-conference on Time Series (ITISE)
Publication date2014
Publication statusPublished - 2014
MoE publication typeA4 Article in conference proceedings

Keywords

  • 512 Business and Management

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    Akusok, A., Veganzones, D., Björk, K-M., Séverin, E., du Jardin, P., Lendasse, A., & Miche, Y. (2014). ELM Clustering – Application to Bankruptcy Prediction. In International work-conference on Time Series (ITISE)