Analysis of Multiple Classifiers Performance for Discretized Data in Authorship Attribution

Baron, Grzegorz

doi:10.1007/978-3-319-59424-8_4

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…It is rarely used in authorship authentication problems. Content-specific and language-specific are noted manually based on the topic and characteristics of language [ 28 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A two level learning model for authorship authentication

2021

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Nowadays, forensic authorship authentication plays a vital role in identifying the number of unknown authors as a result of the world’s rapidly rising internet use. This paper presents two-level learning techniques for authorship authentication. The learning technique is supplied with linguistic knowledge, statistical features, and vocabulary features to enhance its efficiency instead of learning only. The linguistic knowledge is represented through lexical analysis features such as part of speech. In this study, a two-level classifier has been presented to capture the best predictive performance for identifying authorship. The first classifier is based on vocabulary features that detect the frequency with which each author uses certain words. This classifier’s results are fed to the second one which is based on a learning technique. It depends on lexical, statistical and linguistic features. All of the three sets of features describe the author’s writing styles in numerical forms. Through this work, many new features are proposed for identifying the author’s writing style. Although, the proposed new methodology is tested for Arabic writings, it is general and can be applied to any language. According to the used machine learning models, the experiment carried out shows that the trained two-level classifier achieves an accuracy ranging from 94% to 96.16%.

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“…It is rarely used in authorship authentication problems. Content-specific and language-specific are noted manually based on the topic and characteristics of language [ 28 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A two level learning model for authorship authentication

2021

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“…The aim of this step is to extract ''writing style'' features, which are internal characteristics of the text. Surveying authorship attribution studies, these features can be categorized into lexical, character, syntactic, semantic, content-specific, structural and languagespecific [16], [35], [47].…”

Section: ) Authorship Attribution Featuresmentioning

confidence: 99%

“…Thus, this paper aims to bridge the gap and investigates whether applying the ensemble methods lead to improve the accuracy of the AA task in the Arabic language, in addition to selecting the base classifier for ensemble methods and optimal combination of features. Furthermore, since appropriate tuning of the size of the training set and feature data set can render significantly lighter the machine-learning processing [17], [47], this paper gives some recommendations for selecting the optimal settings of data set size that maximizes the accuracy of classifiers.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

et al. 2020

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The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast-growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in the Arabic language, the AA problem has received less attention from the research community due to the complexity and nature of Arabic sentences. The paper presented an intensive review of previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, AdaBoost and Bagging ensemble methods have been applied to Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language. INDEX TERMS Authorship attribution, ensemble methods, stylometric features, TOPSIS method.

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“…They can specify averages, frequencies of occurrence [44], or distributions of selected elements [45], which explains their inherent continuous nature. Inside this group, character features are distinguished, which refer to single characters (letters and digits) or their groups, of either fixed or varied length (but not necessarily forming words [46]). When synonyms and semantic dependencies are studied, with considerations of parts of speech, the resulting style markers can be treated as lexical, placed among syntactic descriptors, or form a separate category of semantic markers.…”

Section: Stylometric Characteristic Featuresmentioning

confidence: 99%

Discretisation of conditions in decision rules induced for continuous data

2020

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Typically discretisation procedures are implemented as a part of initial pre-processing of data, before knowledge mining is employed. It means that conclusions and observations are based on reduced data, as usually by discretisation some information is discarded. The paper presents a different approach, with taking advantage of discretisation executed after data mining. In the described study firstly decision rules were induced from real-valued features. Secondly, data sets were discretised. Using categories found for attributes, in the third step conditions included in inferred rules were translated into discrete domain. The properties and performance of rule classifiers were tested in the domain of stylometric analysis of texts, where writing styles were defined through quantitative attributes of continuous nature. The performed experiments show that the proposed processing leads to sets of rules with significantly reduced sizes while maintaining quality of predictions, and allows to test many data discretisation methods at the acceptable computational costs.

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Analysis of Multiple Classifiers Performance for Discretized Data in Authorship Attribution

Cited by 5 publications

References 15 publications

A two level learning model for authorship authentication

A two level learning model for authorship authentication

Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

Discretisation of conditions in decision rules induced for continuous data

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