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MATLAB toolbox for the multi-label algorithm MLFE

Copyright: 
	Qian-Wen Zhang (cowenzhang@tencent.com, zhangqw@seu.edu.cn),Yun Zhong(zeuszhong@tencent.com),
	Min-Ling Zhang (mlzhang@seu.edu.cn)
	Tencent SPPD, Chengdu 610041, China  &&
	School of Computer Science and Engineering, Southeast University,
	Nanjing 211189, China

This code package can be used freely for academic, non-profit purposes. For other usage, 
please contact us for further information (Prof. Min-Ling Zhang: zhangml@seu.edu.cn ).
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0. Contents
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0. Contents
1. Introduction
2. Requirements
3. Installation
4. How to start?

1.Introduction
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This package implements a novel multi-label algorithm named MLFE (Multi-Label Learning with 
Feature-induced labeling information Enrichment). The learning procedure of MLFE consists of 
three steps, including structural information discovery, labeling information enrichment and 
predictive model induction. Technical details of these steps can be found in the following paper:

[1] Q.-W Zhang, Y. Zhong, M.-L. Zhang. Feature-Induced Labeling Information Enrichment for Multi-Label Learning. 
In: Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, in press.

2. Requirements
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- Matlab, version 2014a and higher.
- The package is mostly self-contain. 
Several functions require the Optimization Toolbox. 

3. Installation
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- Create a directory of your choice and copy the toolbox there.
- Set the path in your Matlab to add the directory you just created.

4. How to start?
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To help you start working with MLFE, we provide a demo(See demo.m) in this package. 
You can follow the procedure as described in the demo.

Please read and play with the demo to get started. Have fun!
