This paper addresses the ongoing work of decision tree on static security assessment of power systems. In this paper efforts are made to accommodate a new approach Probabilistic Fuzzy Decision Tree (PFDT) with the Decision Tree (DT). Here security assessment classification is discussed and the results compared with the conventional method with different operating point are presented. PFDT exami...

Decision tree (DT) has great potential in image semantic learning due to its simplicity in implementation and its robustness to incomplete and noisy data. Decision tree learning naturally requires the input attributes to be nominal (discrete). However, proper discretization of continuous-valued image features is a difficult task. In this paper, we present a decision tree based image semantic le...

In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more than one layer is used. This way, each MLP can be seen as a node of the tree...

As the most important responsibility of purchasing management, the problem of vendor evaluation and selection has always received a great deal of attention from practitioners and researchers. This management decision is a challenge due to the complexity and various criteria involved. This paper presents a hybrid model using data envelopment analysis (DEA), decision trees (DT) and neural network...

To learn the multi-class conceptions from the electroencephalogram (EEG) data we developed a neural network decision tree (DT), that performs the linear tests, and a new training algorithm. We found that the known methods fail inducting the classification models when the data are presented by the features some of them are irrelevant, and the classes are heavily overlapped. To train the DT, our ...

In this paper we use the Bayesian network as a tool of explorative analysis: its theory guarantees that, given the structure and some assumptions, the Markov blanket of a variable is the minimal conditioning set through which the variable is independent from all the others. We use the Markov blanket of a target variable to extract the relevant features for constructing a decision tree (DT). Our...

Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we ...

This paper presents a comparative evaluation of different methods for voice activity detection (VAD). A novel feature set is proposed in order to improve VAD performance in diverse noisy environments. Furthermore, three classifiers for VAD are evaluated. The three classifiers are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and Decision Tree (DT). Experimental results show that th...

Hidden Markov Model (HMM) based speech synthesis using Decision Tree (DT) for duration prediction is known to produce over-averaged rhythm. To alleviate this problem, this paper proposes a two level duration prediction method together with outlier removal. This method takes advantages of accurate regression capability by Extreme Learning Machine (ELM) for phone level duration prediction, and th...

background: we aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (lr), decision tree (dt), artificial neural network (ann), and support vector machine (svm). methods: we used the dataset of a study conducted to predict risk factors of completed suicide in hamadan province, the west of iran, in 2010. to evaluate the high-risk grou...