Author identification using Sequential Minimal Optimization

Jenkins, John; Nick, William; Esterline, Albert; Bloch, J.S.

doi:10.1109/secon.2016.7506654

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Classification of data is a very typical task in data mining. There are large numbers of classifiers that are used to classify the data such as significant directed random walk [12], Bayes classifier [13], Multi-Layer Perceptron [14], Sequential Minimal Optimization [15], etc. Classification also is the process of finding a set of models that describe and differentiate data classes and concepts, to be able to use the model to predict the class whose label is unknown [12].…”

Section: Classification Algorithmmentioning

confidence: 99%

Analysis of Attribute Selection and Classification Algorithm Applied to Hepatitis Patients

Samsuddin

Shah

Saedudin

et al. 2019

International Journal on Advanced Science, Engineering and Information Technology

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Data mining techniques are widely used in classification, attribute selection and prediction in the field of bioinformatics because it helps to discover meaningful new correlations, patterns and trends by sifting through large volume of data, using pattern recognition technologies as well as statistical and mathematical techniques. Hepatitis is one of the most important health problem in the world. Many studies have been performed in the diagnosis of hepatitis disease but medical diagnosis is quite difficult and visual task which is mostly done by doctors. Therefore, this research is conducted to analyse the attribute selection and classification algorithm that applied to hepatitis patients. In order to achieve goals, WEKA tool is used to conduct the experiment with different attribute selector and classification algorithm . Hepatitis dataset that are used is taken from UC Irvine repository. This research deals with various attribute selector namely CfsSubsetEval, WrapperSubsetEval, GainRatioSubsetEval and CorrelationAttributeEval. The classification algorithm that used in this research are NaiveBayesUpdatable, SMO, KStar, RandomTree and SimpleLogistic. The results of the classification model are time and accuracy. Finally, it concludes that the best attribute selector is CfsSubsetEval while the best classifier is given to SMO because SMO performance is better than other classification techniques for hepatitis patients.

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Section: Classification Algorithmmentioning

confidence: 99%

Analysis of Attribute Selection and Classification Algorithm Applied to Hepatitis Patients

Samsuddin

Shah

Saedudin

et al. 2019

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

show abstract

“…Sequential Minimal Optimization (SMO) is one WEKA version of Support Vector Machine (SVM), breaks the training problem into quadratic programming (QP) sub-problems by using heuristics, was suggested by Platt [32]. SMO is an iterative algorithm so chooses the smallest possible sub-problem to solve at every step.…”

Section: B Sequential Minimal Optimizationmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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Abstract-With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

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Investigation of Text Attribution Methods Based on Frequency Author Profile

Diurdeva

Mikhailova

2018

Communications in Computer and Information Science

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Author identification using Sequential Minimal Optimization

Cited by 6 publications

References 7 publications

Analysis of Attribute Selection and Classification Algorithm Applied to Hepatitis Patients

Analysis of Attribute Selection and Classification Algorithm Applied to Hepatitis Patients

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Investigation of Text Attribution Methods Based on Frequency Author Profile

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