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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

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Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination

Xiaoqian Jiang¹, Aditya Menon, Shuang Wang

¹Division of Biomedical Informatics, University California San Diego (UCSD), La Jolla, California, USA. x1jiang@ucsd.edu

|November 10, 2012

Summary

Doubly Optimized Calibrated Support Vector Machine (DOC-SVM) improves clinical decision support by enhancing prediction accuracy and reliability. This novel method achieves better calibration and discrimination than existing models, aiding in more informed medical choices.

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Area of Science:

Computational Biology
Machine Learning
Biostatistics

Background:

Probabilistic models for clinical decision support traditionally prioritize discrimination (ranking error) over calibration (prediction correctness).
Simultaneous optimization of discrimination and calibration is crucial for reliable clinical decision-making.
Existing models often fail to balance these two important aspects effectively.

Purpose of the Study:

To investigate the tradeoffs between discrimination and calibration in probabilistic models.
To develop a unified maximum-margin method for jointly optimizing discrimination and calibration.
To evaluate the performance of the proposed method in breast cancer gene-expression datasets.

Main Methods:

Developed the Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), a novel maximum-margin approach.
DOC-SVM concurrently optimizes two loss functions: ridge regression loss (for calibration) and hinge loss (for discrimination).
Evaluated DOC-SVM on three breast cancer gene-expression datasets (GSE2034, GSE2990, Chanrion's).

Main Results:

DOC-SVM generated significantly more calibrated outputs compared to Support Vector Machine (SVM) and Logistic Regression (LR) across all datasets (p<0.001 to p=0.03).
DOC-SVM demonstrated superior discrimination (higher AUCs) compared to SVM and LR, particularly on Chanrion's dataset (p<0.0001).
The proposed model achieved better calibration without compromising discrimination performance.

Conclusions:

The Doubly Optimized Calibrated Support Vector Machine (DOC-SVM) offers a robust approach for improving probabilistic models in clinical decision support.
DOC-SVM effectively balances model calibration and discrimination, outperforming traditional methods like SVM and LR.
The findings suggest that DOC-SVM can be a valuable tool for enhancing the reliability and utility of predictive models in clinical practice.