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The procedure adopted by authors in developing predictive model is first preprocessed the dataset, then compute F-score values of features, select features with high F-score as discriminative features, then k-means algorithm is used to select feature subset that gives minimum clustering error and finally SVM is used to classification [27] as shown in Fig. 4.
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DTs are a Data Mining Techniques which can be used to solve classification problems.
A subset of samples are used to train classification modules.
Hypothesis were used to establish classification in the test set, which is compared to known one.
Random Forest, Naïve Bayes AdaBoostM1 and MultiLayer Perceptron [16, 17] are used to perform classification.
ANN is also used to solve classification problems in the field of vibrational spectroscopy [76, 77].
Additionally, four types of binary fingerprints were used to develop classification models using SVM based machine learning approach.
Support vector machines (SVMs) are machine learning algorithms widely used to solve classification problems.
This method is commonly used to reduce classification bias and estimate future model performance [40].
SSL is straightforwardly used to smooth classification results.
Such methods include supervised machine learning (ML) techniques, which are used to build classification models.
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