Use of Support Vector Machine, decision tree and Naive Bayesian techniques for wind speed classification

Abstract

In the latest years, pattern recognition, data mining, decision making, and networking have been used as new technologies for automatic classification problems. Classification techniques are needed to predict group membership for data instances. This entire advance tends to process raw data and extract information to obtain knowledge in order to make decisions and solve problems with less human aid. Many of the studies proposed in the literature are based on artificial intelligence (AI) techniques such as Artificial Neural Network (ANN), Fuzzy Logic (FL), Expert System (ES), etc. These techniques use feature vectors derived from disturbance waveforms to classify events. ANN has attracted a great deal of attention among these techniques because of their ability to handle noisy data and their learning capabilities. The disadvantage of neural networks is that they are notoriously slow, especially in the training phase but also in the application phase. Another significant disadvantage of neural networks is that it is very difficult to determine how the net is making its decision. Support Vector Machine (SVM) which is quite a new method and used in this work can overcome these deficiencies and provide efficient and powerful classification algorithms that are capable of dealing with high-dimensional input features and with theoretical bounds on the generalization error and sparseness of the solution provided by statistical learning theory.