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3rd International Conference on Information and Communication Technology ICoICT

作者： Pinem, Asry Faidhul Ashaari Setiawan, Erwin Budi Telkom Univ Computat Sci Dept Bandung Indonesia

ISBN: (纸本)9781479977529

Intrusion detection system is a system that can detect the presence intrusion or attack in a computer network. There are 2 type of intrusion detection system that misuse/signature detection and anomaly detection. This research use a combination of classification and regression tree (CART) and Fuzzy Logic method is used to detect intrusion or attack. CART is used to build rule or model that will be implemented by fuzzy inference engine. Testing process is performed using Fuzzy Logic without doing defuzzification because the resulting rule will be used as a classification. Training, testing and validation of the model is done by using KDD Cup 1999 dataset that has been through the preprocessing and cleaning data process. Accuracy testing and validation is calculated by using the confusion matrix. From several test performed, the best model is built from training 70%, the depth of tree 11 and node leaf minimum percentage 90% with an accuracy was 85,68% and average time validation was 21,92 second.

关键词： Intrusion Detection System Fuzzy Logic classification and regression tree Misuse Detection Anomaly Detection

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Assessing the Predictive Utility of Logistic regression, classification and regression tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct

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AMERICAN JOURNAL OF CRIMINAL JUSTICE 2015年第1期40卷 47-74页

作者： Ngo, Fawn Govindu, Ramakrishna Agarwal, Anurag Univ South Florida Sarasota Manatee Coll Arts & Sci 8350 N Tmiami Trail Sarasota FL 34243 USA Univ South Florida Sarasota Manatee Coll Business Sarasota FL 34243 USA

This study assesses the relative utility of a traditional regression approach logistic regression (LR) - and three classification techniques - classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and multi-layer perceptron neural network (MLPNN)-in predicting inmate misconduct. The four models were tested using a sample of inmates held in state and federal prisons and predictors derived from the importation model on inmate adaptation. Multivalidation procedure and multiple evaluation indicators were used to evaluate and report the predictive accuracy. The overall accuracy of the four models varied between 0.60 and 0.66 with an overall AUC range of 0.60-0.70. The LR and MLPNN methods performed significantly better than the CART and CHAID techniques at identifying misbehaving inmates and the CHAID method outperformed the CART approach in classifying defied inmates. The MLPNN method performed significantly better than the LR technique in predicting inmate misconduct among the training samples.

关键词： Actuarial risk assessment techniques Comparative statistical techniques Logistic regression classification and regression tree Chi-squared automatic interaction detection Neural networks Importation model Inmate misconduct

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A Study on estimation of demolition waste generation index by using CART(classification and regression tree) analysis

A Study on estimation of demolition waste generation index b...

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2015 International Conference on Structural,Mechanical and Materials Engineering(ICSMME2015)

作者： Gi.Wook Cha Won.Hwa Hong Kang.Guk Lee School of Architectural Civil Environmental and Energy Engineering Kyungpook National University

This study aims to predict amounts of waste generated from detached house by their types and materials when they are constructed and *** achieve this objective, required data was collected based on material information of the buildings before *** using the established database, CART analysis was conducted, with the buildings' types and materials as analysis factors, to identify what affects the generation of waste concrete and to estimate indicators of waste *** results were as ***, the most influential factor on generation of waste concrete was types of ***, generation of waste concrete of RC-type and wood-type buildings was not affected by materials, while that of masonry-type buildings was affected by roof ***-type buildings were divided into two categories by roof materials: i) buildings with slab and slab with roof tiles as roof materials and ii) buildings with roof tiles and slate as roof ***, amounts of waste concrete of RC-type and wood-type buildings were 0.324m/m and 0.018 m/m, respectively, and those of masonry-type buildings with roof materials of slab and slab with roof tiles and the other masonrytype buildings were 0.127 m/m and 0.040 m/m, respectively.

关键词： A Study on estimation of demolition waste generation index by using CART analysis classification and regression tree

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Estimation of forest attributes in the Hyrcanian forests, comparison of advanced space-borne thermal emission and reflection radiometer and satellite poure I'observation de la terre-high resolution grounding data by multiple linear, and classification and regression tree regression models

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷 083632-1-083632-17页

作者： Kalbi, Siavash Fallah, Asghar Shataee, Shaban Sari Agr & Nat Resource Univ Dept Forestry Sari 578 Iran Gorgan Agr & Nat Resource Gorgan 386 Iran

Estimation of structural forest attributes, such as volume, basal area, and tree density using a combination of remote sensing and field data, is currently considered a favored option compared to only using field survey data. In a comparative study, multiple linear regression (MLR) and classification and regression tree (CART) models were used to estimate volume, basal area, and tree density using advanced space-borne thermal emission and reflection radiometer (ASTER) and satellite poure I'observation de la terre (SPOT)-high resolution grounding (HRG) imagery in the Darabkola forests, located at the Hyrcanian region of Iran. Results showed that the CART model using SPOT-HRG data achieved the best overall performance for all three forest structural attributes, with adjusted R-2 - 0.746 and RMSE - 67.9 m(3) ha(-1) for volume, adjusted R-2 = 0.771 and RMSE = 3.94 m(2) ha(-1) for basal area, and adjusted R-2 = 0.871 and RMSE = 34.71 nha(-1) for tree density. In general, the CART model, using both ASTER and SPOT-HRG data, produced better estimates of forest attributes compared to the MLR model. In addition, results showed that forest attribute estimations using SPOT-HRG were better than those obtained from ASTER data. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Hyrcanian forests forest attributes advanced space-borne thermal emission and reflection radiometer satellite poure I'observation de la terre-high resolution grounding classification and regression tree linear regression

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classification and regression trees for Symbolic Data

Classification and Regression Trees for Symbolic Data

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作者： Zou, Wanxue University of Georgia

学位级别：Ph.D., Doctor of Philosophy

Compared to classical data which take a single value, there is another type of data, symbolic data, which can be a list, an interval, and even a distribution into consideration. Symbolic data are very common in our daily life; however, the analysis methods for symbolic data are very limited. For instance, a famous and useful method for supervised learning such as regression or classification is the decision tree. There are many useful algorithms based on the decision tree. However, the decision tree is only useful to classic data taking a single value, either numerical or categorical. In this dissertation, I will extend the classification and regression tree method (CART) to symbolic data.

关键词： classification and regression tree Decision tree Interval-valued data Modal multi-valued data Symbolic data

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Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics

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JOURNAL OF HYDROLOGY 2025年 646卷

作者： Fischer, Svenja Pahlow, Markus Singh, Shailesh Kumar Wageningen Univ & Res Hydrol & Environm Hydraul Wageningen Netherlands Univ Canterbury Dept Civil & Nat Resources Engn Christchurch New Zealand Natl Inst Water & Atmospher Res Christchurch New Zealand

A major source of uncertainty in flood statistics are the different flood generation processes. These make the assumption of homogeneous samples questionable. To overcome this issue, a framework for assessing the influence of catchment and climate attributes on flood-generating processes and their effect on flood statistics has been developed and applied to 252 catchments in New Zealand. Mean daily discharge data time series with a length ranging from 20 to 81 years were used. Flood events were classified according to their hydrograph shape. Three types were considered based on the different forcing: heavy rainfall of short duration (termed R1), moderate rainfall of medium intensity and duration (R2), and long-duration rainfall sequences of usually larger spatial extent (R3). The dominant flood type in each catchment was then linked to catchment and climate attributes. This allowed to identify the impact of each flood type on flood statistics and how the flood types have changed over time. The main drivers determining the flood type were rainfall variability and antecedent conditions. Small and steep catchments were dominated by heavy-rainfall floods of shorter duration, while flat and wet catchments were dominated by long-duration floods with larger volumes. Such information can support selection of effective flood protection and management measures.

关键词： Flood type Flood generation Flood statistics classification and regression tree Change-points

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Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model

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ENERGIES 2025年第4期18卷 820-820页

作者： Lee, Kyu-Jeong Choi, So-Won Lee, Eul-Bum Pohang Univ Sci & Technol POSTECH Grad Inst Ferrous & Eco Mat Technol Pohang 37673 South Korea Pohang Iron & Steel Co POSCO Investment & Engn Dept Energy Technol Sect Pohang 37754 South Korea Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn Pohang 37673 South Korea

The by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz-Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry.

关键词： steel mill steam power plant burners boiler combustion optimization combustion control by-product gases gross heat loss prediction advanced boiler combustion control model machine learning random forest classification and regression tree

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Distance-based classification and regression trees for the analysis of complex predictors in health and medical research

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STATISTICAL METHODS IN MEDICAL RESEARCH 2021年第9期30卷 2085-2104页

作者： Johns, Hannah Bernhardt, Julie Churilov, Leonid Ctr Res Excellence Stroke Rehabil & Brain Recover Heidelberg Vic Australia Florey Inst Neurosci & Mental Hlth Heidelberg Vic 3084 Australia Univ Melbourne Melbourne Med Sch Parkville Vic Australia

Predicting patient outcomes based on patient characteristics and care processes is a common task in medical research. Such predictive features are often multifaceted and complex, and are usually simplified into one or more scalar variables to facilitate statistical analysis. This process, while necessary, results in a loss of important clinical detail. While this loss may be prevented by using distance-based predictive methods which better represent complex healthcare features, the statistical literature on such methods is limited, and the range of tools facilitating distance-based analysis is substantially smaller than those of other methods. Consequently, medical researchers must choose to either reduce complex predictive features to scalar variables to facilitate analysis, or instead use a limited number of distance-based predictive methods which may not fulfil the needs of the analysis problem at hand. We address this limitation by developing a Distance-Based extension of classification and regression trees (DB-CART) capable of making distance-based predictions of categorical, ordinal and numeric patient outcomes. We also demonstrate how this extension is compatible with other extensions to CART, including a recently published method for predicting care trajectories in chronic disease. We demonstrate DB-CART by using it to expand upon previously published dose-response analysis of stroke rehabilitation data. Our method identified additional detail not captured by the previously published analysis, reinforcing previous conclusions. We also demonstrate how by combining DB-CART with other extensions to CART, the method is capable of making predictions about complex, multifaceted outcome data based on complex, multifaceted predictive features.

关键词： classification and regression tree cart distance stroke

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Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data

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IEEE ACCESS 2025年 13卷 15795-15811页

作者： Kiran, A. Rubini, P. Kumar, S. Saravana CMR Univ Dept CSE SOET Bengaluru 562149 Karnataka India CMR Univ PG Sch Dept IT SOET Bengaluru 562149 Karnataka India

Automation is the core transformation strategy that every industry wants to get on its roadmap today. Artificial Intelligence (AI) and Machine Learning (ML) are the key components of automation. It is increasingly used in both data analysis and building predictive models from the data. Growing privacy concerns, data confidentiality, and disclosure risks have posed a challenge to the accessibility of right and meaningful data. Several privacy-preserving and disclosure-limiting techniques have come up through research. One such disclosure limiting technique is Synthetic Data. Early research efforts have shown that synthetic data is an effective substitute for real data which can be effectively used to train AI and ML models. However, this needs a comprehensive evaluation before the data user can be confident enough that it is indeed a good substitute for real data. In this paper, we look at three main parameters of synthetic data which should provide a holistic assessment of the quality of synthetic data. First and foremost, how well synthetic data can preserve privacy and control disclosure, second is how good is its utility, and third, are they able to give fair results without any bias when used in machine learning. We review the existing literature to understand various disclosure control limiting methods, synthetic data generators, and then the validation methodologies and evaluation techniques. We understand how data privacy, utility and the fairness of synthetic data intervene with each other and identify the areas for future work.

关键词： Synthetic data Data privacy Privacy Machine learning Reviews Surveys Protection Differential privacy Uncertainty Imputation Artificial intelligence machine learning synthetic data statistical disclosure control differential privacy privacy enhancing technology classification and regression tree fairness SMOTE

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Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines

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NEURAL COMPUTING & APPLICATIONS 2024年第4期36卷 1681-1698页

作者： Shirani Faradonbeh, Roohollah Vaisey, Will Sharifzadeh, Mostafa Zhou, Jian Curtin Univ WA Sch Mines Minerals Energy & Chem Engn Kalgoorlie WA 6430 Australia Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China

The rockburst hazard induced by the extreme release of the stress concentrated in rock mass in deep underground mines poses a significant threat to the safety and economy of the mining projects. Therefore, properly managing this hazard is critical for ensuring rock engineering projects' sustainability. This study proposes comprehensible and practical classifiers for rockburst risk level appraisal by hybridizing K-means clustering with gene expression programming, GEP, logistic regression, LR, and classification and regression tree, CART (i.e., K-mean-GEP-LR and K-means-CART classifiers). A database containing 246 rockburst events with four risk levels of none, light, moderate, and severe was compiled from previous practices. Preliminary statistical analyses were conducted to detect the extreme outliers and determine the critical rockburst indicators. The K-means clustering analysis was performed to identify the main clusters within the database and relabel the rockburst events. The GEP algorithm was then utilized to develop binary models for predicting the occurrence of each class. Then, the likelihood of each class occurrence was determined using LR. Furthermore, the K-means clustering was combined with the CART algorithm to provide another visual tree structure model. The classifiers' performance evaluation showed 96% and 95% accuracy values in the training and testing stages, respectively, for the K-means-GEP-LR model, while the accuracy values of 98.8% and 93.0% were obtained for the foregoing stages for the K-means-CART classifier. The results showed the robustness and high classification capability of both models. MatLab codes were also provided for the K-means-GEP-LR model, which assists other researchers/engineers in implementing the model in practice.

关键词： Rockburst risk level Gene expression programming Logistic regression classification and regression tree Multi-class classification

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