|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A novel image segmentation approach for wood plate surface defect classification through convex optimization显示文摘Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect images, we used convex optimization(CO) with different weights as a pretreatment method for smoothing and the Otsu segmentation method to obtain the target defect area images. Structural similarity(SSIM) results between original image and defect image were calculated to evaluate the performance of segmentation with different convex optimization weights. The geometric and intensity features of defects were extracted before constructing a classification and regression tree(CART) classifier. The average accuracy of the classifier is 94.1% with four types of defects on Xylosma congestum wood plate surface: pinhole, crack,live knot and dead knot. Experimental results showed that CO can save the edge of target defects maximally, SSIM can select the appropriate weight for CO, and the CART classifier appears to have the advantages of good adaptability and high classification accuracy. | Zhanyuan Chang Jun Cao Yizhuo Zhang | 2018 | Journal of Forestry Research2018，29，6： | 14 |
| 2 | Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models显示文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR. | CHEN Tao ZHU Li NIU Rui-qing TRINDER C John PENG Ling LEI Tao | 2020 | Journal of Mountain Science2020，17，3： | 7 |
| 3 | Decision tree and deep learning based probabilistic model for character recognition显示文摘One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, is handwritten character recognition. The common issues in the character recognition are often due to different writing styles, orientation angle, size variation(regarding length and height), etc. This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree(HDT) and deep neural network(DNN). In feature extraction, the local gradient features that include histogram oriented gabor feature and grid level feature, and grey level co-occurrence matrix(GLCM) features are extracted. Then, the extracted features are concatenated to encode shape, color, texture, local and statistical information, for the recognition of characters in the image by applying the extracted features to the hybrid classifier. In the experimental analysis, recognition accuracy of 96% is achieved. Thus, it can be suggested that the proposed model intends to provide more accurate character recognition rate compared to that of character recognition techniques used in the literature. | A.K.Sampath Dr.N.Gomathi | 2017 | Journal of Central South University2017，24，12： | 6 |
| 4 | Gingival enlargements: Differential diagnosis and review of literature显示文摘Gingival enlargement is one of the frequent features of gingival diseases. However due to their varied presentations, the diagnosis of these entities becomes challenging for the clinician. They can be categorized based on their etiopathogenesis, location, size, extent, etc. Based on the existing knowledge and clinical experience, a differential diagnosis can be formulated. Subsequently, after detailed investigation, clinician makes a final diagnosis or diagnosis of exclusion. A perfect diagnosis is critically important, since the management of these lesions and prevention of their recurrence is completely dependent on it. Furthermore, in some cases where gingival enlargement could be the primary sign of potentially lethal systemic diseases, a correct diagnosis of these enlargements could prove life saving for the patient or at least initiate early treatment and improve the quality of life. The purpose of this review article is to highlight significant findings of different types of gingival enlargement which would help clinician to differentiate between them. A detailed decision tree is also designed for the practitioners, which will help them arrive at a diagnosis in a systematic manner. There still could be some lesions which may present in an unusual manner and make the diagnosis challenging. By knowing the existence of common and rare presentations of gingival enlargement, one can keep a broad view when formulating a differential diagnosis of localized(isolated, discrete, regional) or generalized gingival enlargement. | Amit Arvind Agrawal | 2015 | World Journal of Clinical Cases2015，3，9： | 6 |
| 5 | Assessing the performance of decision tree and neural network models in mapping soil properties显示文摘To build any spatial soil database, a set of environmental data including digital elevation model(DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field observations and laboratory analyses data with the results obtained from qualitative and quantitative models. So far, various techniques have been developed for soil data processing. The performance of Artificial Neural Network(ANN) and Decision Tree(DT) models was compared to map out some soil attributes in Alborz Province, Iran. Terrain attributes derived from a DEM along with Landsat 8 ETM+, geomorphology map, and the routine laboratory analyses of the studied area were used as input data. The relationships between soil properties(including sand, silt, clay, electrical conductivity, organic carbon, and carbonates) and the environmental variables were assessed using the Pearson Correlation Coefficient and Principle Components Analysis. Slope, elevation, geomforms, carbonate index, stream network, wetness index, and the band’s number 2, 3, 4, and 5 were the most significantly correlated variables. ANN and DT did not show the same accuracy in predicting all parameters. The DT model showed higher performances in estimating sand(R^2=0.73), silt(R^2=0.70), clay(R^2=0.72), organic carbon(R^2=0.71), and carbonates(R^2=0.70). While the ANN model only showed higher performance in predicting soil electrical conductivity(R^2=0.95). The results showed that determination the best model to use, is dependent upon the relation between the considered soil properties with the environmental variables. However, the DT model showed more reasonable results than the ANN model in this study. The results showed that before using a certain model to predict variability of all soil parameters, it would be better to evaluate the efficiency of all possible models for choosing the best fitted model for each property. In other words, most of the developed models are sitespecific and may not be applicable to use for predicting other soil properties or other area. | Fatemeh HATEFFARD Payam DOLATI Ahmad HEIDARI Ali Asghar ZOLFAGHARI | 2019 | Journal of Mountain Science2019，16，8： | 5 |
| 6 | Discovering unusual structures from exception using big data and machine learning techniques显示文摘Recently, machine learning(ML) has become a widely used technique in materials science study. Most work focuses on predicting the rule and overall trend by building a machine learning model. However,new insights are often learnt from exceptions against the overall trend. In this work, we demonstrate that how unusual structures are discovered from exceptions when machine learning is used to get the relationship between atomic and electronic structures based on big data from high-throughput calculation database. For example, after training an ML model for the relationship between atomic and electronic structures of crystals, we find AgO2 F, an unusual structure with both Ag3+and O22à, from structures whose band gap deviates much from the prediction made by our model. A further investigation on this structure might shed light into the research on anionic redox in transition metal oxides of Li-ion batteries. | Jianshu Jie Zongxiang Hu Guoyu Qian Mouyi Weng Shunning Li Shucheng Li Mingyu Hu Dong Chen Weiji Xiao Jiaxin Zheng Lin-Wang Wang Feng Pan | 2019 | Science Bulletin2019，64，9： | 5 |
| 7 | Extracting impervious surfaces from multi-source satellite imagery based on unified conceptual model by decision tree algorithm显示文摘Extraction of impervious surfaces is one of the necessary processes in urban change detection.This paper derived a unified conceptual model (UCM) from the vegetation-impervious surface-soil (VIS) model to make the extraction more effective and accurate.UCM uses the decision tree algorithm with indices of spectrum and texture,etc.In this model,we found both dependent and independent indices for multi-source satellite imagery according to their similarity and dissimilarity.The purpose of the indices is to remove the other land-use and land-cover types (e.g.,vegetation and soil) from the imagery,and delineate the impervious surfaces as the result.UCM has the same steps conducted by decision tree algorithm.The Landsat-5 TM image (30 m) and the Satellite Probatoire d’Observation de la Terre (SPOT-4) image (20 m) from Chaoyang District (Beijing) in 2007 were used in this paper.The results show that the overall accuracy in Landsat-5 TM image is 88%,while 86.75% in SPOT-4 image.It is an appropriate method to meet the demand of urban change detection. | QIAO Yu,LIU HuiPing,BAI Mu,WANG XiaoDong & ZHOU XiaoLuo School of Geography,Beijing Normal University,Beijing 100875,China | 2010 | Science China(Technological Sciences)2010，53，S1： | 4 |
| 8 | A Hybrid Time Frequency Response and Fuzzy Decision Tree for Non-stationary Signal Analysis and Pattern Recognition显示文摘A Fourier kernel based time-frequency transform is a proven candidate for non-stationary signal analysis and pattern recognition because of its ability to predict time localized spectrum and global phase reference characteristics.However,it suffers from heavy computational overhead and large execution time.The paper,therefore,uses a novel fast discrete sparse S-transform(SST)suitable for extracting time frequency response to monitor non-stationary signal parameters,which can be ultimately used for disturbance detection,and their pattern classification.From the sparse S-transform matrix,some relevant features have been extracted which are used to distinguish among different non-stationary signals by a fuzzy decision tree based classifier.This algorithm is robust under noisy conditions.Various power quality as well as chirp signals have been simulated and tested with the proposed technique in noisy conditions as well.Some real time mechanical faulty signals have been collected to demonstrate the efficiency of the proposed algorithm.All the simulation results imply that the proposed technique is very much efficient. | N.R.Nayak P.K.Dash R.Bisoi | 2019 | International Journal of Automation and computing2019，16，3： | 3 |
| 9 | Automated brain tumor segmentation on multi-modal MR image using SegNet显示文摘The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation.Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only timeconsuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3 D data sets for four MRI modalities(Flair, T1, T1 ce, and T2)for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation.Taking the combined feature as input, a decision tree(DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017(BraTS 2017)challenge, we achieved F-measure scores of 0.85, 0.81,and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively.Experimental results demonstrate that using SegNet models with 3 D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation. | Salma Alqazzaz Xianfang Sun Xin Yang Len Nokes | 2019 | Computational Visual Media2019，5，2： | 3 |
| 10 | Safety validation of decision trees for hepatocellular carcinoma显示文摘AIM: To evaluate a different decision tree for safe liver resection and verify its efficiency.METHODS: A total of 2457 patients underwent hepatic resection between January 2004 and December 2010 at the Chinese PLA General Hospital,and 634 hepatocellular carcinoma(HCC) patients were eligible for the final analyses. Post-hepatectomy liver failure(PHLF) was identified by the association of prothrombin time < 50% and serum bilirubin > 50 μmol/L(the '50-50' criteria),which were assessed at day 5 postoperatively or later. The Swiss-Clavien decision tree,Tokyo University-Makuuchi decision tree,and Chinese consensus decision tree were adopted to divide patients into two groups based on those decision trees in sequence,and the PHLF rates were recorded.RESULTS: The overall mortality and PHLF rate were 0.16% and 3.0%. A total of 19 patients experienced PHLF. The numbers of patients to whom the SwissClavien,Tokyo University-Makuuchi,and Chinese consensus decision trees were applied were 581,573,and 622,and the PHLF rates were 2.75%,2.62%,and 2.73%,respectively. Significantly more cases satisfied the Chinese consensus decision tree than the Swiss-Clavien decision tree and Tokyo University-Makuuchi decision tree(P < 0.01,P < 0.01); nevertheless,the latter two shared no difference(P = 0.147). The PHLF rate exhibited no significant difference with respect to the three decision trees.CONCLUSION: The Chinese consensus decision tree expands the indications for hepatic resection for HCC patients and does not increase the PHLF rate compared to the Swiss-Clavien and Tokyo UniversityMakuuchi decision trees. It would be a safe and effective algorithm for hepatectomy in patients with hepatocellular carcinoma. | Xian-Qiang Wang Zhe Liu Wen-Ping Lv Ying Luo Guang-Yun Yang Chong-Hui Li Xiang-Fei Meng Yang Liu Ke-Sen Xu Jia-Hong Dong | 2015 | World Journal of Gastroenterology2015，21，31： | 3 |
| 11 | 流域不透水面及其变化信息提取(英文)显示文摘Impervious surface is one of the important parameters of valley water circular simulation,scientific estimation for which has significant and practical value for the urban water quantity and process simulation,diffuse pollution estimating and the forecast of climate changes.The objective of this research is to get the information of impervious surface and its dynamic change.Through the computer-assisted field method,the technologies of decision tree and data mining were applied to withdraw the impervious surface information in research region by the Landsat TM data in 1988,1994 and 2002.The results suggested that the accuracy of impervious surface information extraction in the study area arrived above 94.4% in 2002 image.On this basis,the mixed method was used to extract the location and the types of the impervious surface change.The overall accuracy of monitoring reached 89%,which meets the demand of the hydrological models. | 马雪梅 李希峰 | 2008 | Agricultural Science & Technology2008，9，6： | 3 |
| 12 | 基于电子商务用户行为的同义词识别显示文摘该文研究了电子商务领域同义词的自动识别问题。电子商务领域的同义词是指对同一事物或概念的不同表达,即在商品描述和检索中可以相互替换的词,针对该领域新词多、错别字多、近义词多的特点,提出基于用户行为的同义词识别方法。首先通过并列关系符号切分商品标题和基于SimRank思想聚集查询两种方法获取候选集合,进而获取两词的字面特征以及标题、查询、点击等用户行为特征,然后借助Gradient Boost Decision Tree模型判断是否同义。实验表明同义词识别准确率达到56.52%。 | 张书娟 董喜双 关毅 | 2012 | 中文信息学报2012，26，3： | 2 |
| 13 | Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant显示文摘Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis. | Yue Zhao Francesco Di Maio Enrico Zio Qin Zhang Chun-Ling Dong Jin-Ying Zhang | 2017 | Nuclear Science and Techniques2017，28，3： | 2 |
| 14 | Complementary parametric probit regression and nonparametric classi?cation tree modeling approaches to analyze factors affecting severity of work zone weather-related crashes显示文摘Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions. | Ali Ghasemzadeh Mohamed M.Ahmed | 2019 | Journal of Modern Transportation2019，27，2： | 1 |
| 15 | Mapping paddy rice in Jiangsu Province,China,based on phenological parameters and a decision tree model显示文摘Timely and accurate mapping of rice planting areas is crucial under China's current cropping structure. This study proposes a new paddy rice mapping method by combining phenological parameters and a decision tree model.Six phenological parameters were developed to identify paddy rice areas based on the analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS)Enhanced Vegetation Index (EVI)time series and the Land Surface Water Index (LSWI)time series.The six phenological parameters considered the performance of different land cover types during specific phenological phases (EVI1 and EVI2),one-half of or the entire rice growing cycle (LSWI1 and LSWI2),and the shape of the LSWI time series (KurtosisLSWI and SkewnessLSWI).A hierarchical decision tree model was designed to classify paddy rice areas according to the potential separability of different land cover types in paired phenological parameter spaces.Results showed that the decision tree model was more sensitive to LSWI1,LSWI2,and SkewnessLswi than the other phenological parameters.A paddy rice map of Jiangsu Province for 2015 was generated with an optimal threshold set of(0.4,0.42,9,19,1.5,-1.7,0.0)with a total accuracy of 93.9%.The MODIS-derived paddy rice map generally agreed with the paddy land fraction map from the National Land Cover Dataset project,but there were regional discrepancies because of their different definitions of land use and the inability of MODIS to map paddy rice at a fragmental level.The MODIS-derived paddy rice map showed high correlation (R^2=0.85)with county-level agricultural statistics.The results of this study indicate that the phenological parameter-based paddy rice mapping algorithm could be applied at larger spatial scales. | Jianhong LIU Le LI Xin HUANG Yongmei LIU Tongsheng LI | 2019 | Frontiers of Earth Science2019，13，1： | 1 |
| 16 | Efficient privacy-preserving classification construction model with differential privacy technology显示文摘To address the problem of privacy disclosure during data mining, a new privacy-preserving decision tree classification construction model based on a differential privacy-protection mechanism is presented. An efficient classifier that uses feedback to add two types of noise via Laplace and exponential mechanisms to perturb the calculation results are introduced to the construction algorithm that provides a secure data access interface for users.Different split solutions for attributes of continuous and discrete values are provided and used to optimize the search scheme to reduce the error rate of the classifier. By choosing an available quality function with lower sensitivity for making decisions and improving the privacy budget allocation methods, the algorithm effectively resists malicious attacks that depend on the background knowledge. The potential problem of obtaining personal information by guessing unknown sensitive nodes of tree-type data is solved correspondingly. The better privacy preservation and accuracy of this new algorithm are shown by simulation experiments. | Lin Zhang Yan Liu Ruchuan Wang Xiong Fu Qiaomin Lin | 2017 | Journal of Systems Engineering and Electronics2017，28，1： | 1 |
| 17 | Intelligent Information Management and Knowledge Discovery in Large Numeric and Scientific Databases显示文摘IntelligentInformationManagementandKnowledgeDiscoveryinLargeNumericandScientificDatabasesPatrickPerrin;FrederickE.Petry&Willi... | Patrick Perrin Frederick E. Petry & William Thomason(Center for Intelligent and Knowledge-Based Systems)(Computer Science Department, Tulane University, New Orleans LA) | 1996 | Journal of Systems Engineering and Electronics1996，7，2： | 0 |
| 18 | A 4-Corner Codes Classifier Based on Decision Tree Inductive Learning for Handwritten Chinese Characters显示文摘The classification for handwritten Chinese character recognition can be viewed as a transformation in discrete vector space. In this paper, from the point of discrete vector space transformation, a new 4-corner codes classifier based on decision tree inductive learning algorithm ID3 for handwritten Chinese characters is presented. With a feature extraction controller, the classifier can reduce the number of extracted features and accelerate classification speed. Experimental results show that the 4-corner codes classifier performs well on both recognition accuracy and speed. | 钱国良 王亚东 舒文豪 | 1998 | Journal of Harbin Institute of Technology(New Series)1998，5，2： | 0 |
| 19 | A New Speculative Execution Algorithm Based on C4.5 Decision Tree for Hadoop显示文摘As a distributed computing platform, Hadoop provides an effective way to handle big data. In Hadoop, the completion time of job will be delayed by a straggler. Although the definitive cause of the straggler is hard to detect, speculative execution is usually used for dealing with this problem, by simply backing up those stragglers on alternative nodes. In this paper, we design a new Speculative Execution algorithm based on C4.5 Decision Tree, SECDT, for Hadoop. In SECDT, we speculate completion time of stragglers and also of backup tasks, based on a kind of decision tree method: C4.5 decision tree. After we speculate the completion time, we compare the completion time of stragglers and of the backup tasks, calculating their differential value, and selecting the straggler with the maximum differential value to start the backup task.Experiment result shows that the SECDT can predict execution time more accurately than other speculative execution methods, hence reduce the job completion time. | Yuanzhen Li Qun Yang Shangqi Lai Bohan Li | 2015 | 国际计算机前沿大会会议论文集2015，，1： | 0 |
| 20 | 基于决策树的DDoS攻击检测方法研究显示文摘采用决策树中的ID3算法,提出一种基于数据挖掘技术的DDoS攻击检测方法.该方法从被监控网络采集的数据中提取网络流量特征设计检测系统,较好地解决了网络流量分析中数值属性特征的分类问题.实验结果表明该系统能有效检测网络中发生的DDoS攻击行为. | 胡滨 代昆玉 | 2009 | 山东师范大学学报(自然科学版)2009，24，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员