Abstract
The significance of data mining has experienced dramatic growth over the past few years. This growth has been so drastic that many industries and academic disciplines apply data mining in some form. Data mining is a broad subject that encompasses several topics and problems; however this paper will focus on the supervised learning classification problem and discovering ways to optimize the classification process. Four classification techniques (naive Bayes, support vector machine, decision tree, and random forest) were studied and applied to data sets from the UCI Machine Learning Repository. A Classification Learning Toolbox (CLT) was developed using the R statistical programming language to analyze the date sets and report the relationships and prediction accuracy between the four classifiers.
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References
Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, Boston (2006)
Khoury, M.J.: Public health approach to big data in the age of genomics: how can we separate signal from noise? Centers for Disease Control and Prevention (2014). http://blogs.cdc.gov/genomics/2014/10/30/public-health-approach/
Donalek, C.: Supervised and Unsupervised Learning (2011). http://www.astro.caltech.edu/~george/aybi199/Donalek_Classif.pdf
An Introduction to Bayes Theorem. Bayes Theorem: Introduction. http://www.trinity.edu/cbrown/bayesweb/
Leyton-Brown, K.: Reasoning Under Uncertainty: Marginal and Conditional Independence. http://www.cs.ubc.ca/~kevinlb/teaching/cs322%20-%202006-7/Lectures/lect25.pdf
Investopedia US, A Division of IAC.http://www.investopedia.com/terms/p/posterior-probability.asp
Sayad, S.: An Introduction to Data Mining (2015). http://www.saedsayad.com/
Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml
Little, M.A., McSharry, P.E., Roberts, S.J., Costaello, D., Moroz, I.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed. Eng. Online 6(23) (2007). doi:10.1186/1475-925X-6-23
Yeh, I., Yang, K.J., Ting, T.: Knowledge discovery on RFM model using Bernoulli sequence. Expert Syst. Appl. 36, 5866–5871 (2008)
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: Misc Functions of the Department of Statistics (e1071). Institute for Statistics and Mathematics of WU, TU Wien (2014). http://cran.r-project.org/web/packages/e1071/e1071.pdf
Cutler, A., Breiman, L., Liaw A., Wiener, M.: RandomForest: Breiman and Cutler’s Random Forests for Classification and Regression. Institute for Statistics and Mathematics (2015). http://cran.r-project.org/web/packages/randomForest/randomForest.pdf
Khun, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L.: Classification and Regression Training. Institute for Statistics and Mathematics (2015). http://cran.r-project.org/web/packages/caret/caret.pdf
Therneau, T., Atkinson, B., Ripley, B.: Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone. Institute for Statistics and Mathematics (2015). http://cran.r-project.org/web/packages/rpart/rpart.pdf
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. 16, 321–357 (2002)
Hamilton, H.: Confusion Matrix (2012). http://www2.cs.uregina.ca/~ dbd/cs831/notes/confusion_matrix/confusion_matrix.html
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This work was supported via Grant provided by the Rowan University Mathematics Department.
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Ezekiel, W., Thayasivam, U. (2015). A Comparison of Supervised Learning Techniques for Clustering. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_52
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DOI: https://doi.org/10.1007/978-3-319-26532-2_52
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