Speeding up rendering of hybrid surface and volume models

Hybrid rendering of volume and polygonal model is an interesting feature of visualization systems, since it helps users to better understand the relationships between internal structures of the volume and fitted surfaces as well as external surfaces. Most of the existing bibliography focuses at the problem of correctly integrating in depth both types of information. The rendering method proposed in this paper is built on these previous results. It is aimed at solving a different problem: how to efficiently access to selected information of a hybrid model. We propose to construct a decision tree (the Rendering Decision Tree), which together with an auxiliary run-length representation of the model avoids visiting unselected surfaces and internal regions during a traversal of the model.Postprint (published version

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Decision Support System Using Decision Tree and Neural Networks

Decision making in a complex and dynamically changing environment of the present day demands a new techniques of computational intelligence for building equally an adaptive, hybrid intelligent decision support system. In this paper, a Decision Tree-Neuro Based model was developed to handle loan granting decision support system and clinical decision support system(Eye Disease Diagnosis) which are two important decision problems that requires delicate care. The system uses an integration of Decision Tree and Artificial Neural Networks with a hybrid of Decision Tree algorithm and Multilayer Feed-forward Neural Network with backpropagation learning algorithm to build up the proposed model. Different representative cases of loan applications and eye disease diagnosis were considered based on the guidelines of different banks in Nigeria and according to patient complaint, symptoms and physical eye examinations to validate the model. Object-Oriented Analysis and Design (OO-AD) methodology was used in the development of the system, and an object-oriented programming language was used with a MATLAB engine to implement the models and classes designed in the system. The system developed, gives 88% success rate and eliminate the opacity of an ordinary neural networks system. Keywords: Decision Tree-Neuro Based Model, Backpropagation Learning Algorithm, Object-Oriented Analysis and Design, MATLAB Embedded Engine, Loan Granting, Eye Diseases Diagnosis

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches

Naïve Bayes Decision Tree Hybrid Approach for Intrusion Detection System

Internet is also increasing exponentially increasing intrusion or attacks by crackers exploit vulnerabilities in Internet protocols, operating systems and software applications. Intrusion or attacks against computer networks, especially the Internet has increased from year to year. Intrusion detection systems into the main stream in the information security. The main purpose of intrusion detection system is a computer system to help deal with the attack. This study presents a hybrid approach to decision tree algorithm and naïve Bayes to detect computer network intrusions. Performance is measured based on the level of accuracy, sensitivity, precision and spesificity. Dataset used in this study is a dataset KDD 99 intrusion detection system. Dataset is composed of two training data and testing data. The selection of attributes is done using the chi-square, selected the top ten attributes based on the calculation of chi-square. From the experimental results obtained by the accuracy of naïve Bayes decision tree algorithm was 99.82%

Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.Comment: See http://www.jair.org/ for any accompanying file