Discrimination of authorship using visualization
Information Processing and Management: an International Journal
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Mining e-mail content for author identification forensics
ACM SIGMOD Record
Text classification using string kernels
The Journal of Machine Learning Research
Style mining of electronic messages for multiple authorship discrimination: first results
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic text categorization in terms of genre and author
Computational Linguistics
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Language independent authorship attribution using character level language models
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Applying Authorship Analysis to Extremist-Group Web Forum Messages
IEEE Intelligent Systems
Journal of the American Society for Information Science and Technology
Linguistic profiling for author recognition and verification
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Tensor Space Models for Authorship Identification
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
Cuisine: Classification using stylistic feature sets and-or name-based feature sets
Journal of the American Society for Information Science and Technology
Expert Systems with Applications: An International Journal
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
Combining integrated sampling with SVM ensembles for learning from imbalanced datasets
Information Processing and Management: an International Journal
Author identification in bengali literary works
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Comparing alternative classifiers for database marketing: The case of imbalanced datasets
Expert Systems with Applications: An International Journal
A new document author representation for authorship attribution
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
A novel probabilistic feature selection method for text classification
Knowledge-Based Systems
The use of orthogonal similarity relations in the prediction of authorship
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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Authorship analysis of electronic texts assists digital forensics and anti-terror investigation. Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. Moreover, in this task usually there is no similarity between the distribution of training and test texts over the classes, that is, a basic assumption of inductive learning does not apply. In this paper, we present methods to handle imbalanced multi-class textual datasets. The main idea is to segment the training texts into text samples according to the size of the class, thus producing a fairer classification model. Hence, minority classes can be segmented into many short samples and majority classes into less and longer samples. We explore text sampling methods in order to construct a training set according to a desirable distribution over the classes. Essentially, by text sampling we provide new synthetic data that artificially increase the training size of a class. Based on two text corpora of two languages, namely, newswire stories in English and newspaper reportage in Arabic, we present a series of authorship identification experiments on various multi-class imbalanced cases that reveal the properties of the presented methods.