ELM Clustering – Application to Bankruptcy Prediction

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

ELM Clustering – Application to Bankruptcy Prediction

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

Finance, Helsinki

Arcada University of Applied Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-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 language	English
Title of host publication	International work-conference on Time Series (ITISE)
Publication date	2014
Publication status	Published - 2014
MoE publication type	A4 Article in conference proceedings

Keywords

512 Business and Management

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title = "ELM Clustering – Application to Bankruptcy Prediction",

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. ",

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AU - Akusok, Anton

AU - Veganzones, David

AU - Björk, Kaj-Mikael

AU - Séverin, Eric

AU - du Jardin, Philippe

AU - Lendasse, Amaury

AU - Miche, Yoan

PY - 2014

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N2 - 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.

AB - 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.

KW - 512 Business and Management

M3 - Conference contribution

BT - International work-conference on Time Series (ITISE)

ER -

ELM Clustering – Application to Bankruptcy Prediction

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