Comparing approaches to prepare data in classification problems

Gonçalvès, Paulo; Barros, Roberto Souto Maior de

doi:10.1109/aiccsa.2011.6126613

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Phase 3: Results Analysis1

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2017

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“…The reason is to investigate the result of classification accuracy by using several techniques by using the same dataset. Several researchers such as Gonçalves and Graczyk et al [45]Barros [46], and Zainal et al [47] have examined the accuracy result between these two tools and the result accuracy is not same. But, the result is still acceptable when comparing both data mining tools; RapidMiner and WEKA on both SMS datasets.…”

Section: Phase 3: Results Analysismentioning

confidence: 99%

A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

Foozy

Ahmad

Abdollah

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.

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Section: Phase 3: Results Analysismentioning

confidence: 99%