hi from the above mentioned study provide the conclusions

Process Process Documents from Dan Parameters Tokonto non lettere mote le 01 Filter Toms rotor Tokens (by Longmi min chars She man har TUS Stom (Porter Feter Stopwords (English) 30 Figure 3: Process text suboperators Several models for classification were used: k-NN, WHIB1 (Weka classifier) and Naive Bayes (Table 1). It turned out that using operator Naive Bayes takes shorter time for execution, but accuracy gained is lower comparing with two other operators (operator W-1B1 and operator Naive Bayes). Operator W-B1 achieves precision as k-NN, but W-IB1 operator needs lot more time. So, k-NN operator, based on principle of neuron network, is used in this case. For assessment of optimal number "k" the Optimize Parameters node was used while for measure type "Cosine Similarity has been used. For model evaluation, operator Validation(x-Validation) has been used. Number of validation is setup to 5 and the sample was stratified. Criteria of goodness was accuracy" (according to contest propositions). The best result for predicting accuracy using operator K-NN was 0.53 on 6% of sample set (Figure 4). For operator Naive Bayes best accuracy achieved (in best case) is 0.22 Table 1: Comparing result of 3 operators MODEL KNN K=1 K = 2 K3 K4 Time 1:59 2:00 1:52 1:37 16:43 0:31 0:32 Accuracy 49,39% +/- 1.29% 49.46% +/- 1.29% 51.06% +/- 0.57% 50.38% +/- 0.68% accuracy: 49,20% 4/- 1.099 20.37% +/-0.93% 20.37% +/-0.93% W-IB1 Naive Bayes Laplace correction