Thetaskofextractingtheusedfeaturevectorinminingtasks(classification,clustering...etc.)is consideredthemostimportanttaskforenhancingthetextprocessingcapabilities.Thispaperproposes anovelapproachtobeusedinbuildingthefeaturevectorusedinwebtextdocumentclassification process;addingsemanticsinthegeneratedfeaturevector.Thisapproachisbasedonuti...

This article presents the machine learning approach used by the University of Wolverhampton in the GREC-NEG’09 task. A classifier based on J48 decision tree and a meta-classifier were used to produce two runs. Evaluation on the development set shows that the metaclassifier achieves a better performance.

Support Vector Machines (SVM) is a state-of-the-art, powerful algorithm in machine learning which has strong regularization attributes. Regularization points to the model generalization to the new data. Therefore, SVM can be very efficient for spam detection. Although the experimental results represent that the performance of SVM is usually more than other algorithms, but its efficiency is decr...

The present study has proposed three novel hybrid models by integrating traditional ensemble models, such as random forest, logitboost, and naive bayes, six newly developed of rotation forest (RF), decision tree (RF-DT), J48 (DF-J48), bayes (RF-NBT), neural network (RF-NN), M5P (RF-M5P) REPTree (RF-REPTree), with statistical i.e. weight evidence, logistic regression combination WOE LR. To predi...