Extreme Learning Machine prediction under high class imbalance in bioinformatics

TADEO RODRIGUEZ; LEANDRO EZEQUIEL DI PERSIA; GEORGINA STEGMAYER; DIEGO HUMBERTO MILONE

Cordoba

Conferencia; Simposio Latinoamericano de Investigación de Operaciones e Inteligencia Artificial - Conferencia Latinoamericana de Estudios en Informática; 2017

CEntro Latinoamericano de Estudios en Informática - Sociedad Argentina de Investigación Operativa

Class imbalance in machine learning is when there are significantly fewer training instances of one class in comparison to another one. In bioinformatics, there is such a problem in the computational predictionof novel microRNA (miRNAs) within a full genome. The well-known precursors miRNA (pre-miRNA) are usually only a few in comparison to the hundreds of thousands of potential candidates, which makes thistask a high class imbalance classification problem. It is well-known that high class imbalance usually affects any classical supervised machine learning classifier. Thus the imbalance must be explicitly considered.Extreme Learning Machine (ELM) is a supervised artificial neural network model that has gained interest in the last years because of its high learning rate and performance. In this work, we propose a novelapproach to overcome the high class imbalance in pre-miRNAs prediction data in which ELMs are used for predicting good candidates to pre-miRNA, without needing balanced data sets. Real datasets were used for validation of the proposal with several class imbalance levels. The results obtained showed the superiority of the ELM approach against very recent state-ofthe-art methods in the same experimental conditions.