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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Seera, Manjeevan Loo, Chu Kiong Lim, Chee Peng Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

ISBN: (纸本)9781479938407

In this paper, the application of a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) to human activity recognition is presented. The hybrid FMM-CART model capitalizes the merits of both FMM and CART in data classification and rule extraction. To evaluate the effectiveness of FMM-CART, two data sets related to human activity recognition problems are conducted. The results obtained are higher than those reported in the literature. More importantly, practical rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. This outcome positively indicates the potential of FMM-CART in undertaking human activity recognition tasks.

关键词： classification and regression tree fuzzy min-max neural network human activity recognition rule extraction

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Remote Sense Image classification Based on CART Algorithm

Remote Sense Image Classification Based on CART Algorithm

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3rd International Conference on Energy, Environment and Sustainable Development (EESD 2013)

作者： Yang, Bao Hua Li, Shuang Anhui Agr Univ Sch Informat & Comp Hefei 230036 Peoples R China

ISBN: (纸本)9783037859735

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

关键词： Remote sensing image classification and regression tree classification

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Movie Box Office Inteval Forecasting Based on CART

Movie Box Office Inteval Forecasting Based on CART

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International Symposium on Computational Intelligence and Design

作者： Zhenlong Zhang Jianping Chai Bo Li Yan Wang Min An Zhougui Deng Information Engineering School Communication University of China China Beijing China

ISBN: (纸本)9781467395885

This paper constructs a model for the forecasting of movie box office revenues during the pre-production period. Our study selects some basic variables in film forecasting field such as production country, genre, seasonality and star value. This paper divides the box office revenue into seven intervals ranged from 'flop' to 'blockbuster' and chooses classification and regression tree to predict box office in China by comparing different models. The study sets the top 100 films of 2014 in box revenues as the training set and the top 50 films of the former 5 months in 2015 as the testing set. The model produces excellent forecasting accuracy in both training and testing sets. In our model, the fitting degree of training set is 99%, and the prediction accuracy value is 76%. The importance of variables indicates that the appeal of directors and stars is notable in China.

关键词： forecast movie box office revenues intervals pre-production classification and regression tree

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Diagnosis of gastric cancer using near-infrared Raman spectroscopy and classification and regression tree techniques

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JOURNAL OF BIOMEDICAL OPTICS 2008年第3期13卷 34013-1-34013-8页

作者： Teh, Seng Khoon Zheng, Wei Ho, Khelk Yu Teh, Ming Yeoh, Khay Guan Huang, Zhiwei Natl Univ Singapore Fac Engn Dept Bioengn Bioimaging Lab Singapore 117576 Singapore Natl Univ Singapore Hosp Dept Med Yoo Loo Lin Sch Med Singapore 119260 Singapore

The purpose of this study is to apply near-infrared (NIR) Raman spectroscopy and classification and regression tree (CART) techniques for identifying molecular changes of tissue associated with cancer transformation. A rapid-acquisition NIR Raman system is utilized for tissue Raman spectroscopic measurements at 785-nm excitation. 73 gastric tissue samples (55 normal, 18 cancer) from 53 patients are measured. The CART technique is introduced to develop effective diagnostic algorithms for classification of Raman spectra of different gastric tissues. 80% of the Raman dataset are randomly selected for spectral learning, while 20% of the dataset are reserved for validation. High-quality Raman spectra in the range of 800 to 1800 cm(-1) are acquired from gastric tissue within 5 s. The diagnostic sensitivity and specificity of the learning dataset are 90.2 and 95.7%;and the predictive sensitivity and specificity of the independent validation dataset are 88.9 and 92.9%, respectively, for separating cancer from normal. The tissue Raman peaks at 875 and 1745 cm-1 are found to be two of the most significant features to discriminate gastric cancer from normal tissue. NIR Raman spectroscopy in conjunction with the CART technique has the potential to provide an effective and accurate diagnostic means for cancer detection in the gastric system. (C) 2008 society of Photo-Optical Instrumentation Engineers.

关键词： cancer diagnosis near-infrared Raman spectroscopy stomach classification and regression tree

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Exploring correlations in gene expression microarray data for maximum predictive-minimum redundancy biomarker selection and classification

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1437-1443页

作者： Arevalillo, Jorge M. Navarro, Hilario Univ Nacl Educ Distancia Dept Stat Operat Res & Numer Anal E-28040 Madrid Spain

An important issue in the analysis of gene expression microarray data is concerned with the extraction of valuable genetic interactions from high dimensional data sets containing gene expression levels collected for a small sample of assays. Past and ongoing research efforts have been focused on biomarker selection for phenotype classification. Usually, many genes convey useless information for classifying the outcome and should be removed from the analysis;on the other hand, some of them may be highly correlated, which reveals the presence of redundant expressed information. In this paper we propose a method for the selection of highly predictive genes having a low redundancy in their expression levels. The predictive accuracy of the selection is assessed by means of classification and regression trees (CART) models which enable assessment of the performance of the selected genes for classifying the outcome variable and will also uncover complex genetic interactions. The method is illustrated throughout the paper using a public domain colon cancer gene expression data set. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Microarray data Gene expression Biomarker selection classification and prediction Redundancy classification and regression tree

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Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

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APPLIED SOFT COMPUTING 2013年第12期13卷 4493-4507页

作者： Seera, Manjeevan Lim, Chee Peng Ishak, Dahaman Singh, Harapajan Intel Technol Bayan Lepas Penang Malaysia Deakin Univ Waurn Ponds Vic Australia Univ Sains Malaysia Nibong Tebal Penang Malaysia Univ Teknol MARA Permatang Pauh Penang Malaysia

In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the classification and regression tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz, broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fault detection and diagnosis Fuzzy Min-Max neural network classification and regression tree Induction motor Motor Current Signature Analysis

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Comparison of CART- and MLP-based power system transient stability preventive control

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期45卷 129-136页

作者： Lin, Yu-Jen I Shou Univ Dept Elect Engn Kaohsiung Taiwan

This paper compares the power system transient stability preventive controls based on classification and regression tree (CART) and multilayer perceptron (MLP). CART is a branch of the decision trees (DTs) algorithms, which have been applied extensively in the literature for preventive control design due to their transparent character of interpreting inference procedures. Recent research has shown that multilayer perceptron (MLP), a member of artificial neural networks (ANNs) family, can also be employed to design preventive control since explaining internal mapping relationship becomes possible. This paper first briefs the essentials of CART and MLP, and then follows the numerical study on a three-generator six-bus power system. Transient stability preventive controls are designed by CART and MLP, and are compared afterwards. The finding shows that preventive controls developed by both approaches think alike. They are even complementary. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Transient stability Preventive control classification and regression tree Multilayer perceptron

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Regional Patterns of Major Nonnative Invasive Plants and Associated Factors in Upper Midwest Forests

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FOREST SCIENCE 2013年第1期59卷 38-49页

作者： Fan, Zhaofei Moser, W. Keith Hansen, Mark H. Nelson, Mark D. Mississippi State Univ Dept Forestry Mississippi State MS 39762 USA US Forest Serv USDA No Res Stn Morgantown WV USA

Nonnative invasive plants (IPs) are rapidly spreading into natural ecosystems (e.g., forests and grasslands). Potential threats of IP invasion into natural ecosystems include biodiversity loss, structural and environmental change, habitat degradation, and economic losses. The Upper Midwest of the United States encompasses the states of Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, and Wisconsin, a region populated with 46 million people. Concerns of IP threats to the productive timberlands in the region have emerged with rapid expansion of urban areas and associated land cover changes caused by increasing human disturbances. Using the strategic inventory data from the 2005-2006 US Department of Agriculture Forest Service Forest Inventory and Analysis program and other data such as forestland cover and transportation coverage/layers, we modeled the regional patterns of IPs by using a combination of nonparametric techniques, including classification and regression tree analysis, kernel density smoothing, and bootstrapping. For the Midwest region, a probability map and historical records of human-related introduction of IPs of interest suggests that invasive shrubs, herbs, and grasses were initially introduced into the central (sparsely forested) areas and then spread north and south (densely forested areas), whereas invasive vines spread primarily from the south into other parts of the region. The probability of IPs in densely forested areas (0.1) was one-fifth of that in sparsely forested areas. Shrubs are the predominant IP threat and are distributed across the vast region with the exception of the northern part. Invasive grasses and herbs are most abundant in the central part of the region, and invasive vines are most common in the southern part. Percent forest cover and road proximity (distance to roads) as indicators of anthropogenic disturbances, were the most significant drivers of IP occurrence/abundance. Site factors, including forest productivity a

关键词： nonnative invasive plant Forest Inventory Analysis kernel density smoothing probability classification and regression tree bootstrap

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A Novel Decision tree Approach Based on Transcranial Doppler Sonography to Screen for Blunt Cervical Vascular Injuries

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JOURNAL OF ULTRASOUND IN MEDICINE 2013年第6期32卷 1023-1031页

作者： Purvis, Dianna Aldaghlas, Tayseer Trickey, Amber W. Rizzo, Anne Sikdar, Siddhartha George Mason Univ Dept Neurosci Fairfax VA 22030 USA George Mason Univ Dept Bioengn Fairfax VA 22030 USA Inova Fairfax Hosp Dept Trauma Serv Fairfax VA USA

Objectives-Early detection and treatment of blunt cervical vascular injuries prevent adverse neurologic sequelae. Current screening criteria can miss up to 22% of these injuries. The study objective was to investigate bedside transcranial Doppler sonography for detecting blunt cervical vascular injuries in trauma patients using a novel decision tree approach. Methods-This prospective pilot study was conducted at a level I trauma center. Patients undergoing computed tomographic angiography for suspected blunt cervical vascular injuries were studied with transcranial Doppler sonography. Extracranial and intracranial vasculatures were examined with a portable power M-mode transcranial Doppler unit. The middle cerebral artery mean flow velocity, pulsatility index, and their asymmetries were used to quantify flow patterns and develop an injury decision tree screening protocol. Student t tests validated associations between injuries and transcranial Doppler predictive measures. Results-We evaluated 27 trauma patients with 13 injuries. Single vertebral artery injuries were most common (38.5%), followed by single internal carotid artery injuries (30%). Compared to patients without injuries, mean flow velocity asymmetry was higher for single internal carotid artery (P = .003) and single vertebral artery (P = .004) injuries. Similarly, pulsatility index asymmetry was higher in single internal carotid artery (P = .015) and single vertebral artery (P = .042) injuries, whereas the lowest pulsatility index was elevated for bilateral vertebral artery injuries (P = .006). The decision tree yielded 92% specificity, 93% sensitivity, and 93% correct classifications. Conclusions-In this pilot feasibility study, transcranial Doppler measures were significantly associated with the blunt cervical vascular injury status, suggesting that transcranial Doppler sonography might be a viable bedside screening tool for trauma. Patient-specific hemodynamic information from transcranial Doppler ass

关键词： blunt cervical vascular injury classification and regression tree transcranial Doppler sonography trauma

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Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction

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INFORMATION SCIENCES 2013年 236卷 174-185页

作者： Chuang, Chun-Ling Kai Nan Univ Dept Informat Management Tao Yuan 338 Taiwan

Predicting business failure is an important and challenging issue that has served as an impetus for many academic studies over the past three decades. This study aims at developing CBR-based hybrid models of predicting business failure. The need to supplement CBR (Case-Based Reasoning) with other classification and diagnosis techniques is triggered by the fact that accuracy and effectiveness tend to get reduced when CBR alone is applied to handle too many attributes. To enhance the accuracy of bankruptcy prediction, the hybrid models developed by this study include: RST-CBR (combining Rough Set Theory with CBR), RST-GRA-CBR (integrating RST, Grey Relational Analysis, and CBR), and CART-CBR (combining classification and regression tree with CBR). In order to verify the ability of the proposed models to achieve optimal accuracy rate, this study further compares the predictive ability of CBR with those of other comparative models. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Decision analysis Data mining Rough set theory Business failure prediction Grey relation analysis classification and regression tree

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