Decision Tree |
It has this name due to the appearance of a tree (Han & Kamber, 2006Han, J., & Kamber, M. (2006). Data mining: concepts and techniques (3rd ed.). São Francisco: Elsevier.). It is constructed with the root, decision and leaf nodes, which are the questions, and the branches that are the answers (Larose, 2005Larose, D. (2005). Discovering knowledge in data: an introduction to data mining (1st ed.). Hoboken: Wiley.). The algorithms occur in three steps (Groth, 2000Groth, R. (2000). Data mining: building competitive strategy (2nd ed.). Nova Jersey: Prentice-Hall.): i) definition of dependent and independent variables from a data source; ii) examination of the impact of each variable on the result; and iii) definition of the variable that predicts the results of the other variables. The algorithms suggested by Jurka et al. (2013)Jurka, T. P., Collingwood, L., Boydstun, A. E., Grossman, E., & Atteveldt, W. (2013). RTextTools: a supervised learning package for text classification. The R Journal, 5(1), 6-12. http://dx.doi.org/10.32614/RJ-2013-001. http://dx.doi.org/10.32614/RJ-2013-001...
; were BAGGING (Breiman, 1996Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. http://dx.doi.org/10.1007/BF00058655. http://dx.doi.org/10.1007/BF00058655...
), RF (Liaw & Wiener, 2002Liaw, A., & Wiener, M. (2002). Classification and regression by randon forest. R News, 2(3), 18-22.) and TREE (Breiman et al., 1984Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees (1st ed.). Wadsworth: Chapman & Hall.). |
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Bayesian Classification |
They predict the probability of the data belonging to certain classes. This technique is based on the Bayes' theorem and assumes that the data are independent of each other (Han & Kamber, 2006Han, J., & Kamber, M. (2006). Data mining: concepts and techniques (3rd ed.). São Francisco: Elsevier.). Jurka et al. (2013)Jurka, T. P., Collingwood, L., Boydstun, A. E., Grossman, E., & Atteveldt, W. (2013). RTextTools: a supervised learning package for text classification. The R Journal, 5(1), 6-12. http://dx.doi.org/10.32614/RJ-2013-001. http://dx.doi.org/10.32614/RJ-2013-001...
suggests the use of the BOOSTING algorithm (Freund & Schapire, 1997Freund, Y., & Schapire, R. (1997). A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. http://dx.doi.org/10.1006/jcss.1997.1504. http://dx.doi.org/10.1006/jcss.1997.1504...
). |
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Neural Networks |
This technique simulates the functioning of the human brain, as a function of the large number of neurons, enabling learning based on experience (Larose, 2005Larose, D. (2005). Discovering knowledge in data: an introduction to data mining (1st ed.). Hoboken: Wiley.). There are at least two types of neural networks: percepetron and multilayer (Tan et al., 2009Tan, P., Steinbach, M., & Kumar, V. (2009). Introdução ao data mining (1. ed.). Rio de Janeiro: Moderna.). The algorithm SLDA (Blei & McAuliffe, 2010Blei, D., & McAuliffe, J. (2010). Supervised topic models. In Anais da 20º Conferência Internacional de Sistemas de Processamento de Informações Neurais (pp. 121-128). Vancouver: ACM.) is suggested by Jurka et al. (2013)Jurka, T. P., Collingwood, L., Boydstun, A. E., Grossman, E., & Atteveldt, W. (2013). RTextTools: a supervised learning package for text classification. The R Journal, 5(1), 6-12. http://dx.doi.org/10.32614/RJ-2013-001. http://dx.doi.org/10.32614/RJ-2013-001...
to classify with this technique. |
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SVM |
The support vector machine raises training data to a higher dimension by looking for an optimal separation hyperplane (a greater distance separating different classes) (Feldman & Sanger, 2007Feldman, R., & Sanger, J. (2007). The text mining handbook (1st ed.). Nova York: Cambridge University Press.; Han & Kamber, 2006Han, J., & Kamber, M. (2006). Data mining: concepts and techniques (3rd ed.). São Francisco: Elsevier.). The SVM algorithm (Fan et al., 2005Fan, R., Chen, P., & Lin, C. (2005). Working set selection using second order information for training support vector machines. Journal of Machine Learning Research, 6, 1889-1918.) is that indicated by Jurka et al. (2013)Jurka, T. P., Collingwood, L., Boydstun, A. E., Grossman, E., & Atteveldt, W. (2013). RTextTools: a supervised learning package for text classification. The R Journal, 5(1), 6-12. http://dx.doi.org/10.32614/RJ-2013-001. http://dx.doi.org/10.32614/RJ-2013-001...
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Logistic Regression |
It is a special type of regression, which deals with categorical and independent variables (Groth, 2000Groth, R. (2000). Data mining: building competitive strategy (2nd ed.). Nova Jersey: Prentice-Hall.). In binary classes, probabilities greater than 50% indicate the presence in the class ” 1 ” and probabilities less than 50% indicate the presence in the “0” class (Fuller et al., 2011Fuller, C., Biros, D., & Delen, D. (2011). An investigation of data and text mining methods for real world deception detection. Expert Systems with Applications, 38(7), 8392-8398. http://dx.doi.org/10.1016/j.eswa.2011.01.032. http://dx.doi.org/10.1016/j.eswa.2011.01...
). Jurka et al. (2013)Jurka, T. P., Collingwood, L., Boydstun, A. E., Grossman, E., & Atteveldt, W. (2013). RTextTools: a supervised learning package for text classification. The R Journal, 5(1), 6-12. http://dx.doi.org/10.32614/RJ-2013-001. http://dx.doi.org/10.32614/RJ-2013-001...
suggests the use of the GLMNET algorithm (Friedman et al., 2010Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. http://dx.doi.org/10.18637/jss.v033.i01. PMid:20808728. http://dx.doi.org/10.18637/jss.v033.i01...
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