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A Novel Synthetic Over-Sampling Technique for Imbalanced Classification of Gene Expressions Using Autoencoders and Swarm Optimization

  • Maisa Daoud
  • Michael Mayo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

A new synthetic minority class over-sampling approach for binary (normal/cancer) classification of microarray gene expression data is proposed. The idea is to exploit a previously trained autoencoder in combination with the Particle Swarm Optimisation algorithm to generate new synthetic examples of the minority class for solving the class imbalance problem. Experiments using two different autoencoder representation sizes (500 and 30) and two base classifiers (Support Vector Machine and naïve Bayes) show that the proposed method is able to generate discriminating representations that outperformed state-of-the-art methods such as Synthetic Minority Class Over-sampling Technique and Density-Based Synthetic Minority Class Over-sampling Technique in many test cases.

Keywords

Class imbalance Cancer prediction Autoencoders Classification 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of WaikatoHamiltonNew Zealand

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