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

Li Yanze, Harutoshi Ogai

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ882-887
ページ数6
ISBN(電子版)9781728110899
出版ステータスPublished - 2020 9 23
イベント59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020 - Chiang Mai, Thailand
継続期間: 2020 9 232020 9 26

出版物シリーズ

名前2020 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
国/地域Thailand
CityChiang Mai
Period20/9/2320/9/26

ASJC Scopus subject areas

  • 制御と最適化
  • 器械工学
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 決定科学(その他)
  • 産業および生産工学

フィンガープリント

「Quasi-Linear SVM with Local Offsets for High-dimensional Imbalanced Data Classification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル