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New Combined S-transform and logistic model tree Technique for Recognition and Classification of Power Quality Disturbances

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2011年第1期39卷 80-98页

作者： Moravej, Z. Abdoos, A. A. Pazoki, M. Semnan Univ Dept Elect & Comp Engn Fac Elect & Comp Engn Semnan 13955 Iran

This article presents certain features and a new classification method for recognition of power quality disturbances. The proposed method uses S-transform to extract the most severable features of the power quality waveforms. The disturbances decision-making scheme is performed using the logistic model tree. This new algorithm requires fewer features compared to the wavelet based approach for the identification of power quality events. Thus, the required memory space and learning logistic model tree time are substantially reduced. The most common types of disturbances, including sag, swell, interruption, harmonics, transient, and flicker, are studied. Disturbances consisting of both sag and harmonics, as well as both swell and harmonics, are also considered. The result shows that the classifier can detect and classify different power quality events correctly. Sensitivity of the proposed algorithm under noisy condition is also investigated.

关键词： power quality disturbances feature extraction multi-class pattern recognition logistic model tree S-transform

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Stability risk assessment of slopes using logistic model tree based on updated case histories

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第12期20卷 21229-21245页

作者： Ahmad, Feezan Tang, Xiao-Wei Ahmad, Mahmood Gonzalez-Lezcano, Roberto Alonso Majdi, Ali Arbili, Mohamed Moafak Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Univ Tenaga Nas Inst Energy Infrastruct Kajang 43000 Malaysia Univ Engn & Technol Peshawar Dept Civil Engn Bannu Campus Bannu 28100 Pakistan CEU Univ Univ San Pablo CEU Escuela Politecn Super Dept Architecture & Design Monteprincipe Campus Madrid 28668 Spain Al Mustaqbal Univ Dept Bldg & Construct Tech Engn Hilla 51001 Iraq Erbil Polytech Univ Erbil Tech Engn Coll Dept Tech Civil Engn Erbil 44001 Iraq

A new logistic model tree (LMT) model is developed to predict slope stability status based on an updated database including 627 slope stability cases with input parameters of unit weight, cohesion, angle of internal friction, slope angle, slope height and pore pressure ratio. The performance of the LMT model was assessed using statistical metrics, including accuracy (Acc), Matthews correlation coefficient (Mcc), area under the receiver operating characteristic curve (AUC) and F-score. The analysis of the Acc together with Mcc, AUC and F-score values for the slope stability suggests that the proposed LMT achieved better prediction results (Acc = 85.6%, Mcc = 0.713, AUC = 0.907, F-score for stable state = 0.967 and F-score for failed state = 0.923) as compared to other methods previously employed in the literature. Two case studies with ten slope stability events were used to verify the proposed LMT. It was found that the prediction results are completely consistent with the actual situation at the site. Finally, risk analysis was carried out, and the result also agrees with the actual conditions. Such probability results can be incorporated into risk analysis with the corresponding failure cost assessment later.

关键词： logistic model tree machine learning slope stability risk analysis performance metrics

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The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery

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GEOCARTO INTERNATIONAL 2017年第1期32卷 71-86页

作者： Colkesen, Ismail Kavzoglu, Taskin Gebze Tech Univ Dept Geodet & Photogrammetr Engn Gebze Turkey

logistic model tree (LMT), a new method integrating standard decision tree (DT) induction and linear logistic regression algorithm in a single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied in the context of pixel-and object-based classifications using high-resolution WorldView-2 imagery, and its performance was compared with C4.5, random forest and Adaboost. Results of the study showed that the LMT generally produced more accurate classification results than the other methods for both pixel-and object-based classifications. The improvement in classification accuracy reached to 3% in pixel-based and 5% in object-based classifications. It was also estimated that the LMT algorithm produced the most accurate results considering the allocation and overall disagreement errors. Based on the Wilcoxon's Signed-Ranks tests, the performance differences between the LMT and the other methods were statistically significant for both pixel-and object-based image classifications.

关键词： logistic model tree random forest boosting decision tree object-based classification

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A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

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CATENA 2017年 151卷 147-160页

作者： Chen, Wei Xie, Xiaoshen Wang, Jiale Pradhan, Biswajeet Hong, Haoyuan Bui, Dieu Tien Duan, Zhao Ma, Jianquan Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Univ Putra Malaysia Dept Civil Engn Fac Engn Serdang 43400 Selangor Malaysia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Jiangxi Meteorol Bur Jiangxi Prov Meteorol Observ 109 ShengfuBeier Rd Nanchang 330046 Jiangxi Peoples R China Univ Coll Southeast Dept Business Adm & Comp Sci Geog Informat Syst Grp N-3800 Bo I Telemark Norway

The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances;the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility. (C) 2016 Elsevier B.V. All rights reserved.

关键词： logistic model tree Random forest Classification and regression tree Landslide China

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A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment

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GEOCARTO INTERNATIONAL 2018年第12期33卷 1398-1420页

作者： Chen, Wei Shahabi, Himan Shirzadi, Ataollah Li, Tao Guo, Chen Hong, Haoyuan Li, Wei Pan, Di Hui, Jiarui Ma, Mingzhe Xi, Manna Bin Ahmad, Baharin Xian Univ Sci & Technol Coll Geol & Environm Xian Shaanxi Peoples R China Shandong Univ Sci & Technol Shandong Prov Key Lab Deposit Mineralizat & Sedim Qingdao Peoples R China Univ Kurdistan Dept Geomorphol Fac Nat Resources Sanandaj Iran Univ Kurdistan Dept Rangeland & Watershed Management Fac Nat Resources Sanandaj Iran Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing Jiangsu Peoples R China State Key Lab Cultivat Base Geog Environm Evolut Nanjing Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Res Nanjing Jiangsu Peoples R China UTM Dept Geoinformat Fac Geoinformat & Real Estate Johor Baharu Malaysia

This study addresses landslide susceptibility mapping (LSM) using a novel ensemble approach of using a bivariate statistical method (weights of evidence [WoE] and evidential belief function [EBF])-based logistic model tree (LMT) classifier. The performance and prediction capability of the ensemble models were assessed using the area under the ROC curve (AUROC), standard error, 95% confidence intervals and significance level P. model performance analyses indicated that the AUROC values of the WoE-LMT ensemble model using the training and validation data-sets were 86.02 and 85.9%, respectively, whereas those of the EBF-LMT ensemble model were 88.2 and 87.8%, respectively. On the other hand, the AUC curves for the four landslide susceptibility maps indicated that the AUC values of the ensemble models of WoE-LMT (85.11 and 83.98%) and EBF-LMT (86.21 and 85.23%) could improve the performance and prediction accuracy of single WoE (84.23 and 82.46%) and EBF (85.39 and 81.33%) models for the training and validation data-sets.

关键词： Landslide evidential belief function weight of evidence logistic model tree China

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Rotation Forest-Based logistic model tree for Website Phishing Detection 21st

Rotation Forest-Based Logistic Model Tree for Website Phishi...

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Balogun, Abdullateef O. Akande, Noah O. Usman-Hamza, Fatimah E. Adeyemo, Victor E. Mabayoje, Modinat A. Ameen, Ahmed O. Univ Ilorin Dept Comp Sci PMB 1515 Ilorin Nigeria Univ Teknol PETRONAS Dept Comp & Informat Sci Bandar Seri Iskandar 32610 Perak Malaysia Landmark Univ Dept Comp Sci Omu Aran Kwara State Nigeria Leeds Beckett Univ Sch Built Environm Engn & Comp Headingley Campus Leeds LS6 3QS W Yorkshire England

ISBN: (纸本)9783030870133;9783030870126

The emergence of web and internet technology has led to its use in a broad array of services ranging from financial to educational services. This has led to a spike in the number of cybersecurity problems over the years, the most notable of which is the phishing attack, in which malicious websites imitate legitimate websites to capture gullible users' details needed for unauthorized access. However, current mitigation strategies, such as anti-phishing applications and Machine Learning (ML) methods, have been effective for detecting phishing activities. Hackers, on the other hand, are developing new ways to circumvent these countermeasures. Nevertheless, given the dynamism of phishing attempts, there is a continual demand for innovative and efficient solutions for website phishing detection. This study proposes a Rotation Forest-based logistic model trees (RF-LMT) for website phishing detection. LMT is a technique that combines logistic regression and tree inference into a single model tree. Three datasets of different instance distributions, both balanced and imbalanced, are used to investigate the proposed RF-LMT. From the results, it was observed that LMT performed better than the selected baseline classifiers. This finding revealed that LMT can perform comparably to baseline classifiers. However, in comparison to LMT and experimented baseline classifiers, the proposed RF-LMT method showed superior performance in website phishing detection. Specifically, RF-LMT had a high detection accuracy (98.24%), AUC (0.998), f-measure (0.982) values with a low false-positive rate (0.018). Furthermore, RF-LMT outperformed existing ML-based phishing attack models. As a result, the proposed RF-LMT method is recommended for dealing with complex phishing attacks.

关键词： Cybersecurity logistic model tree Machine learning Phishing attack Rotation forest

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A Comparative Study of Kernel logistic Regression, Radial Basis Function Classifier, Multinomial Naive Bayes, and logistic model tree for Flash Flood Susceptibility Mapping

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WATER 2020年第1期12卷 239-239页

作者： Binh Thai Pham Tran Van Phong Huu Duy Nguyen Qi, Chongchong Al-Ansari, Nadhir Amini, Ata Lanh Si Ho Tran Thi Tuyen Hoang Phan Hai Yen Hai-Bang Ly Prakash, Indra Dieu Tien Bui Univ Transport Technol Hanoi 100000 Vietnam Vietnam Acad Sci & Technol Inst Geol Sci 84 Chua Lang St Hanoi 100000 Vietnam VNU Univ Sci Fac Geog 334 Nguyen Trai Hanoi 100000 Vietnam Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Lulea Univ Technol Dept Civil Environm & Nat Resources Engn S-97187 Lulea Sweden AREEO Kurdistan Agr & Nat Resources Res & Educ Ctr Sanandaj *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Vinh Univ Sch Agr & Resources Dept Resource & Environm Management Nghe An 470000 Vietnam Vinh Univ Sch Social Educ Dept Geog Nghe An 470000 Vietnam Govt Gujarat Dept Sci & Technol Bhaskarcharya Inst Space Applicat & GeoInformat Gandhinagar 382002 India Univ South Eastern Norway Dept Business & IT Geog Informat Syst Grp N-3674 Notodden Norway

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naive Bayes (NBM), and logistic model tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983-0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988;KLR: AUC = 0.985;RBFC: AUC = 0.984;and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

关键词： flash flood kernel logistic regression radial basis function network multinomial naive Bayes logistic model tree machine learning Vietnam

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modeling landslide susceptibility using an evidential belief function-based multiclass alternating decision tree and logistic model tree

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ENVIRONMENTAL EARTH SCIENCES 2022年第15期81卷 404-404页

作者： Zhao, Qifei Chen, Wei Peng, Chaohong Wang, Danzhi Xue, Weifeng Bian, Huiyuan Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Minist Nat Resources Key Lab Coal Resources Explorat & Comprehens Util Xian 710021 Peoples R China Sichuan Inst Geol Engn Invest Grp Co Ltd Chengdu 610072 Peoples R China Inst Water Conservancy Works Design Xuzhou Xuzhou 221002 Jiangsu Peoples R China Shaanxi Coal & Chem Technol Inst Co Ltd Xian 710065 Peoples R China

The primary objective of the present research is to apply and compare the performance of evidential belief function (EBF)-based logistic model trees (LMTs) and multiclass alternate decision trees (LADtrees) in landslide susceptibility mapping in Xiaojin County, China. Firstly, 328 landslides were mapped in the study area. Then, 70% of landslide points were used as training samples randomly, and the remaining 30% were intended for validation samples. For the study area, 12 landslide-related conditioning factors were identified, for instance, plan curvature, profile curvature, elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), land use, lithology, distance to river soil, and distance to roads. The following procedure was to map landslide susceptible regions through EBF, LADtree and LMT models. Finally, the receiver operating characteristic (ROC) curve was utilized to contrast and test the capacity of the three models. The success rates with the training dataset were 0.880, 0.877 and 0.886 for the EBF, LADtree and LMT models, respectively. In addition, their prediction rates with the validation dataset were 0.846, 0.861 and 0.865, respectively. The results could provide references for disaster management and land-use planning.

关键词： Evidential belief function Multiclass alternating decision tree logistic model tree Landslide GIS

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Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region

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ENVIRONMENTAL EARTH SCIENCES 2018年第5期77卷 159-159页

作者： Tien Dat Pham Dieu Tien Bui Yoshino, Kunihiko Le, Nga Nhu Univ Tsukuba Grad Sch Syst & Informat Engn 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan VNUA CARES Hanoi Vietnam Univ Coll Southeast Norway Dept Business & IT Geog Informat Syst Grp Gullbringvegen 36 N-3800 Bo I Telemark Norway Univ Tokyo Fac Agr Dept Biol & Environm Engn Bunkyo Ku 1-1-1 Yayoi Tokyo 1138657 Japan Inst Mech Dept Marine Mech & Environm 264 Doi Can Lieu Giai Ba Dinh Vietnam

The main objective of this study is to map the spatial distribution of mangrove species and assess their changes from 2010 to 2015 in Hai Phong City of Vietnam located on the tropical region using the ALOS PALSAR data and an optimized rulebased decision tree technique. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected and used, and then, spatial distributions of mangrove species were derived using logistic model tree (LMT) classifier. The LMT is current state-of-the-art machine learning method that has not been used for mapping of mangrove species. The results showed that incorporation of ALOS PALSAR imagery and GIS in the LMT algorithm provides satisfactory overall accuracy and kappa coefficient. The ALOS-2 PALSAR for 2015 achieved better overall accuracy, with an increment of 3.6% in mapping mangrove species than that of the ALOS PALSAR for 2010. The ALOS-2 PALSAR-derived model yielded the overall accuracy of 83.8% and the kappa coefficient of 0.81, compared with those of the ALOS PALSAR-derived model, 80.2% and 0.78, respectively. The results of classification for 2010 and 2015 were significantly different using the McNemar test. This research demonstrates the potential use of ALOS PALSAR together with machine learning techniques in monitoring mangrove species in tropical areas.

关键词： ALOS PALSAR Hai Phong City Mangrove species Decision tree GIS logistic model tree

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Comparing the prediction performance of logistic model tree with different ensemble techniques in susceptibility assessments of different landslide types

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GEOCARTO INTERNATIONAL 2022年第26期37卷 14261-14291页

作者： Huang, Junpeng Ma, Ning Ling, Sixiang Wu, Xiyong Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu Peoples R China Southwest Jiaotong Univ Key Lab High Speed Railway Engn Minist Educ Chengdu Peoples R China

Susceptibility based on different landslide types has rarely been assessed. Therefore, this paper aims to compare the prediction performance of hybrid approaches by combining the logistic model tree (LMT) with Decorate, random subspace, and rotation forest ensemble techniques (De-LMT, RS-LMT, and RoF-LMT) for susceptibility assessments of different landslide types. The Yuzi River catchment along the eastern margin of the Tibetan Plateau was selected as the study area. In this catchment, 478 rockfalls and 167 landslides were identified, and further partitioned into training (70%) and validation (30%) datasets, respectively. Subsequently, 13 conditioning factors were initially considered, and then 11 and 8, respectively, were selected as the final rockfall and landslide influencing parameters to conduct susceptibility models through multicollinearity analysis and information gain. Finally, the model performances were evaluated by area under the receiver operating characteristic curves (AUC). The obtained results demonstrated that elevation and rainfall were most influential factors for rockfall and landslide. For the rockfall prediction, the De-LMT model achieved the best prediction accuracy with the highest AUC (0.939), followed by the RS-LMT (0.938), RoF-LMT (0.928), and LMT (0.919) models. For the landslide prediction, the De-LMT model (AUC = 0.931) outperformed and outclassed the RoF-LMT (0.926), RS-LMT (0.922), and LMT (0.915) models. Therefore, it is reasoned out that all models exhibited satisfactory performance (AUC > 0.9), and the De-LMT model was superior in rockfall and landslide spatial prediction.

关键词： Landslide susceptibility logistic model tree ensemble models Decorate landslide typology

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