领域实例迁移的交互文本非平衡情感分类方法

田锋 1; 兰田 1; CHAO Kuo-Ming 3; 吴凡 1; 郑庆华 1; 2; 高鹏达 1; 2

doi:10.7652/xjtuxb201504011

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领域实例迁移的交互文本非平衡情感分类方法

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- 领域实例迁移的交互文本非平衡情感分类方法
- An Unbalanced Emotion Classification Method for Interactive Texts Based on Multiple-Domain Instance Transfer
- 西安交通大学学报 2015年49卷第4期页码：67-72
- 作者机构：
  
  1. 西安交通大学陕西省天地网技术重点实验室,西安,710049
  2. 西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2014-09-11。作者简介: 田锋(1972—),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(61472315);国家科技支撑计划资助项目(2013BAK09B01);教育部“创新团队发展计划”资助项目(IRT13035);国家留学基金资助项目(20133018)
- DOI：10.7652/xjtuxb201504011
  中图分类号： TP391.1
- 网络首发：2015-04-10，
  
  纸质出版：2015
- 稿件说明：
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田锋 1, 兰田 1, CHAO Kuo-Ming 3, 等. 领域实例迁移的交互文本非平衡情感分类方法[J]. 西安交通大学学报, 2015,49(4):67-72.

An Unbalanced Emotion Classification Method for Interactive Texts Based on Multiple-Domain Instance Transfer[J]. 2015, 49(4): 67-72.
田锋 1, 兰田 1, CHAO Kuo-Ming 3, 等. 领域实例迁移的交互文本非平衡情感分类方法[J]. 西安交通大学学报, 2015,49(4):67-72. DOI： 10.7652/xjtuxb201504011.

An Unbalanced Emotion Classification Method for Interactive Texts Based on Multiple-Domain Instance Transfer[J]. 2015, 49(4): 67-72. DOI： 10.7652/xjtuxb201504011.

摘要

针对交互文本句子短、成分缺失、多领域下类分布不均衡导致的高维、特征值稀疏、正样本稀少的难点

提出面向目标数据集实例迁移的数据层面采样方法。该方法提出目标数据集和源数据集共性特征的Top-N信息增益和值占比函数

选择评价两个数据集实例相似度的特征; 提出目标数据集和源数据集特征空间一致性处理方法

克服两者特征空间不一致的问题; 提出分领域的实例选取与迁移方法

克服多领域下的类分布不均衡问题。实验结果表明:该方法有效缓解了交互文本的非平衡问题

使支持向量机、随机森林、朴素贝叶斯、随机委员会4个经典分类算法的加权平均的接收者运行特征曲线(receiver operating characteristic

ROC)指标提升了11.3%。

Abstract

A data level sampling method of target dataset-oriented instance transfer is proposed to solve the problem that the characteristics of interactive texts such as short sentences

missing parts of sentences and unbalanced class distribution in multiple-domains result in difficulties of high dimension

sparse eigenvalue in feature space and lack of positive instances. A function is employed to choose features for evaluating the instance similarity between source and target datasets. The function calculates the sum of the information gains of Top-N common features of these two datasets and their proportions in the sum. Moreover

a homogenization processing method is presented for feature spaces of the target dataset and the source dataset to overcome the feature spaces inconsistency between these two datasets. A method for selecting and transferring instances from a domain of source dataset to the corresponding one of target dataset is adopted to solve the problem of unbalanced class distribution in multiple domains. Experimental results show that the proposed method effectively alleviates the unbalanced problem in target dataset. The proposed method running with four classic classification methods

i.e. support vector machine

random forest

naive Bayes

and random committee

results in an 11.3% improvement in average of weighted receiver operating characteristic curve(ROC).

关键词

Keywords

references

OGURA H, AMANO H, KONDO M. Comparison of metrics for feature selection in imbalanced text classification [J]. Expert Systems with Applications, 2011, 38(5): 4978-4989.

DAI Wenyuan, YANG Qiang, XUE Guirong, et al. Boosting for transfer learning [C]∥Proceedings of the 24th International Conference on Machine Learning. New York, USA: ACM, 2007: 193-200.

TIAN Feng, GAO Pengda, LI Longzhuang, et al. Recognizing and regulating e-learners' emotions based on interactive Chinese texts in e-learning systems [J]. Knowledge-Based Systems, 2014, 55: 148-164.

TIAN Feng, LIANG Huijun, LI Longzhuang, et al. Sentiment classification in turn-level interactive Chinese texts of e-learning applications [C]∥Proceedings of the 2012 IEEE 12th International Conference on Advanced Learning Technologies. Piscataway, NJ, USA: IEEE, 2012: 480-484.

BARANDELA R, VALDOVINOS R M, SÁNCHEZ J S, et al. The imbalanced training sample problem: under or over sampling? [C]∥Proceedings of the IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition. Berlin, Germany: Springer, 2004: 806-814.

CHAWLA N V. C4.5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure [C]∥Proceedings of the International Conference on Machine Learning from Imbalanced Datasets: Ⅱ. New York, USA: ACM, 2003: 1-8.

SUN Yanmin, KAMEL M S, WONG A K C, et al. Cost-sensitive boosting for classification of imbalanced data [J]. Pattern Recognition, 2007, 40(12): 3358-3378.

ZHOU Zhihua, LIU Xuying. Training cost-sensitive neural networks with methods addressing the class imbalance problem [J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 63-77.

WANG Suge, LI Deyu, ZHAO Lidong, et al. Sample cutting method for imbalanced text sentiment classification based on BRC [J]. Knowledge-Based Systems, 2013, 37: 451-461.

LIU Ying, LOH H T, SUN Aixin. Imbalanced text classification: a term weighting approach [J]. Expert Systems with Applications, 2009, 36(1): 690-701.

HAN Jiawei, KAMBER M. 数据挖掘概念与技术 [M]. 2版. 范明, 孟小峰,译. 北京: 机械工业出版社, 2006: 216-217.

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