Quasi-Linear SVM with Local Offsets for High-dimensional Imbalanced Data Classification

Li Yanze, Harutoshi Ogai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Imbalanced problems often occur in the classification problem. A special case is within-class imbalance, which worsen the imbalance distribution problem and increase the learning concept complexity. Most methods for solving imbalanced data classification focus on finding a globe boundary to solve between-class imbalance problem. My thesis proposes a effective quasi-linear network with local offsets adjustment for imbalanced classification problems. First, we proposed a gated piecewise linear network, an autoencoder-based partitioning method is modified for imbalanced datasets to divide input space into multiple linearly separable partitions along the potential separation boundary. Construct a quasi-linear SVM based on the gated signal that obtained by autoencoder partitioning information. Then training a neural network that let F-score as loss function to generate the local offsets on each local cluster. Finally a quasi-linear SVM classifier with local offsets is constructed for the imbalanced datasets. Our proposed method avoids calculating Euclidean distance, so it can be applied to high dimensional datasets. Simulation results on different real world datasets that our method is effective for imbalanced data classification especially in high-dimensional data.

Original languageEnglish
Title of host publication2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages882-887
Number of pages6
ISBN (Electronic)9781728110899
Publication statusPublished - 2020 Sep 23
Event59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020 - Chiang Mai, Thailand
Duration: 2020 Sep 232020 Sep 26

Publication series

Name2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020

Conference

Conference59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
Country/TerritoryThailand
CityChiang Mai
Period20/9/2320/9/26

Keywords

  • F-measure
  • imbalaced data classification
  • kernel composition
  • support vector machine
  • within-class imbalances

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Industrial and Manufacturing Engineering

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