---
title: "A Direct Boosting Approach for Semi-supervised Classification"
authors:
  - "Shaodan Zhai"
  - "Tian Xia"
  - "Zhongliang Li"
  - "Shaojun Wang"
doi: ""
url: "https://ijcai.org/proceedings/2015/565"
pdf: "https://ijcai.org/proceedings/2015/0565.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4033-4039"
track: "Special Track on Machine Learning"
keywords:
---

# A Direct Boosting Approach for Semi-supervised Classification

Shaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 4033-4039.

## Abstract

We introduce a semi-supervised boosting approach (SSDBoost), which directly minimizes the classification errors and maximizes the margins on both labeled and unlabeled samples, without resorting to any upper bounds or approximations. A two-step algorithm based on coordinate descent/ascent is proposed to implement SSDBoost. Experiments on a number of UCI datasets and synthetic data show that SSDBoost gives competitive or superior results over the state-of-the-art supervised and semi-supervised boosting algorithms in the cases that the labeled data is limited, and it is very robust in noisy cases.