Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA

Graczyk, Magdalena; Lasota, Tadeusz; Trawiński, Bogdan

doi:10.1007/978-3-642-04441-0_70

Cited by 52 publications

(25 citation statements)

References 10 publications

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“…So far, the authors of the present paper have investigated several methods to construct regression models: evolutionary fuzzy systems, neural networks, decision trees, and statistical algorithms using MATLAB, KEEL, and WEKA data mining systems (Graczyk et al, 2009;Krzystanek et al, 2009;Król et al, 2008;Lasota et al, 2010), on the basis of a real-world case of real estate appraisals.…”

Section: B Trawiński Et Almentioning

confidence: 99%

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Trawiński¹,

Smźtek²,

Telec³

et al. 2012

International Journal of Applied Mathematics and Computer Science

Self Cite

137

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In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 × N and N × N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.

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Section: B Trawiński Et Almentioning

confidence: 99%

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Trawiński¹,

Smźtek²,

Telec³

et al. 2012

International Journal of Applied Mathematics and Computer Science

Self Cite

137

View full text Add to dashboard Cite

show abstract

“…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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“…Eventhough, Weka support many model evaluation procedures and metrics, but many data survey and visualization methods are absence [6]. Weka also more oriented towards classification and regression problems and less towards descriptive statistics and clustering methods.…”

Section: Wekamentioning

confidence: 98%

A study of open-source data mining tools for forecasting

Hasim

Haris

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

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This paper described five open-sources Data Mining (DM) tools which are Weka, RapidMiner, KEEL, Orange and Tanagra. The features and functionality of these DM tools can be benefited by educators and researchers. The DM algorithms embedded in the tools can be utilized for forecasting. Weka and RapidMiner have most of the desire characteristic for a fully-functional and flexible platform therefore their use can be recommended for most of DM tasks.

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Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA

Cited by 52 publications

References 10 publications

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

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

A study of open-source data mining tools for forecasting

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