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Updated: Jan 15, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Small sphere and large margin support tensor machines for imbalanced tensor data classification.

Hexuan Liu¹, Xiao Li², Yitian Xu¹

¹College of Science, China Agricultural University, Beijing, 100083, China.

Neural Networks : the Official Journal of the International Neural Network Society

|October 8, 2025

Summary

This summary is machine-generated.

A novel small sphere and large margin support tensor machine (SSLMSTM) effectively classifies imbalanced tensor data. This method extends to higher ranks (HR-SSLMSTM), outperforming existing approaches.

Keywords:

CANDECOMP/PARAFAC decomposition Imbalanced data classification Small sphere and large margin Support tensor machine

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Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

Imbalanced data classification is challenging, especially with tensor data.
Existing methods like the small sphere and large margin approach (SSLM) are limited to vector data.

Purpose of the Study:

To propose a novel model for imbalanced tensor data classification.
To leverage the structural information inherent in tensor data for improved classification.

Main Methods:

Introduction of the small sphere and large margin support tensor machine (SSLMSTM).
Construction of two concentric hyperspheres centered by a rank-1 tensor.
Extension to higher rank R cases (HR-SSLMSTM).
Utilizing CANDECOMP/PARAFAC decomposition and alternating iteration for model solving.

Main Results:

SSLMSTM effectively captures normal samples within a small hypersphere and pushes outliers outside a large hypersphere.
Increased margin between hyperspheres enhances performance.
Experiments demonstrate the validity and effectiveness of both SSLMSTM and HR-SSLMSTM.

Conclusions:

SSLMSTM and HR-SSLMSTM offer a robust solution for imbalanced tensor data classification.
The proposed models successfully utilize tensor data structure for superior performance.
This work opens new avenues for handling complex, imbalanced datasets in machine learning.