The WEKA data mining software

Abstract

This paper presents the performance of a classifier built using the stackingC algorithm in nine different data sets. Each data set is generated using a sampling technique applied on the original imbalanced data set. Five new sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling Undersampling, and Combined-Lax Random Undersampling Oversampling) that were based on the three sampling techniques (i.e., Random Undersampling, Random Oversampling, and Synthetic Minority Oversampling Technique) usually used as solutions in imbalance learning. The metrics used to evaluate the classifier's performance were F-measure and G-mean. Fmeasure determines the performance of the classifier for every class, while G-mean measures the overall performance of the classifier. The results using F-measure showed that for the data without a sampling technique, the classifier's performance is good only for the majority class. It also showed that among the eight sampling techniques, RU and LRU have the worst performance while other techniques (i.e., RO, C-LRUO and C-LROU) performed well only on some classes. The best performing techniques in all data sets were SMOTE, SMOTERandRep, and LRO having the lowest Fmeasure values between 0.5 and 0.65. The results using Gmean showed that the oversampling technique that attained the highest G-mean value is LRO (0.86), next is C-LROU (0.85), then SMOTE (0.84) and finally is SMOTERandRep (0.83). Combining the result of the two metrics (F-measure and G-mean), only the three sampling techniques are considered as good performing (i.e., LRO, SMOTE, and SMOTERan-dRep)

Data fusion for paroxysmal events’ classification from EEG

“…We previously evaluated [5] the ability of the extracted features to differentiate epileptic from non-epileptic epochs by examining several classification algorithms implemented by the WEKA machine learning toolkit software [24] including the BayesNet [24,26], RandomCommittee, RandomForest [27], IBk [28] and SMO [29,30] with RBF kernel. Since the overall highest accuracy was achieved by the BayesNet classifier, we now evaluate the examined fusion schemes with respect to the BayesNet.…”

Fluorescence spectroscopy for the detection of potentially malignant disorders and squamous cell carcinoma of the oral cavity

“…This approach could lead to a better comprehension about the role of spectrum intensity in the quality of classification. Three different classifiers were used: Naïve Bayes, k-nearest neighbor (kNN) and a decision tree C4.5 (J48), all of them implemented on open source software WEKA [44]. All classifiers were tested for both excitation wavelengths in the exact same conditions.…”

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

“…As input variables for the development of the classification model, the docking score and the CoMSIA similarity fields, steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor were used. Random tree (RT) classification technique implemented in WEKA 66 program was used to discriminate between the different classes. Decision trees represent a supervised approach to classification with its simple structure consisting of root, nodes, branches and leaves.…”