Decision tree learning ≈ Decision tree learning
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LightGBM: A Highly Efficient Gradient Boosting Decision Tree Open
Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, th…
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Classification Based on Decision Tree Algorithm for Machine Learning Open
Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending…
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Deep Learning for Stock Market Prediction Open
The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Fou…
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A Survey on Decision Tree Algorithms of Classification in Data Mining Open
As the computer technology and computer network technology are developing, the amount of data in information industry is getting higher and higher. It is necessary to analyze this large amount of data and extract useful knowledge from it. …
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Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm* Open
Decision tree is a supervised machine learning algorithm suitable for solving classification and regression problems. Decision trees are recursively built by applying split conditions at each node that divides the training records into sub…
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Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging Open
The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVI…
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Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project Open
Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regress…
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Performance Analysis of Data Mining Classification Techniques to Predict Diabetes Open
Diabetes Mellitus is one of the major health challenges all over the world. The prevalence of diabetes is increasing at a fast pace, deteriorating human, economic and social fabric. Prevention and prediction of diabetes mellitus is increas…
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RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks Open
This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP T…
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Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques Open
Classification is a data mining technique used to predict group membership for data instances within a given dataset. It is used for classifying data into different classes by considering some constrains. The problem of data classification…
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GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment Open
Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash…
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Analysis of Various Decision Tree Algorithms for Classification in Data Mining Open
Today the computer technology and computer network technology has developed so much and is still developing with pace.Thus, the amount of data in the information industry is getting higher day by day.This large amount of data can be helpfu…
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Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree Open
Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past de…
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Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm Open
The research on the educational field that involves Data Mining techniques is rapidly increasing. Applying Data Mining techniques in an educational background are known as Educational Data Mining that aims to discover hidden knowledge and …
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Prediction performance of improved decision tree-based algorithms: a review Open
Applications of machine learning can be found in retail, banking, education, health sectors etc. To process the large data emanating from the various sectors, researchers are developing different algorithms using expertise from several fie…
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Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers Open
Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sens…
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education Open
Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead…
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Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks Open
Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recog…
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Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis Open
The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining…
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Learning invariants using decision trees and implication counterexamples Open
Inductive invariants can be robustly synthesized using a learning model where the teacher is a program verifier who instructs the learner through concrete program configurations, classified as positive, negative, and implications. We propo…
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Decision trees: from efficient prediction to responsible AI Open
This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which…
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Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data Open
Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learn…
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A classification tree to assist with routine scoring of the Clinical Frailty Scale Open
Background the Clinical Frailty Scale (CFS) was originally developed to summarise a Comprehensive Geriatric Assessment and yield a care plan. Especially since COVID-19, the CFS is being used widely by health care professionals without trai…
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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis Open
OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) T…
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Pipe failure modelling for water distribution networks using boosted decision trees Open
Pipe failure modelling is an important tool for strategic rehabilitation planning of urban water distribution infrastructure. Rehabilitation predictions are mostly based on existing network data and historical failure records, both of vary…
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Decision tree SVM model with Fisher feature selection for speech emotion recognition Open
The overall recognition rate will reduce due to the increase of emotional confusion in multiple speech emotion recognition. To solve the problem, we propose a speech emotion recognition method based on the decision tree support vector mach…
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CHAID Decision Tree: Methodological Frame and Application Open
Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into…
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Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer Open
The comparative analysis results indicate that the KNN classifier outperforms the result of the decision-tree classifier in the breast cancer classification..
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Verifiable Reinforcement Learning via Policy Extraction Open
While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinfor…
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Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction Open
For tasks such as medical diagnosis and knowledge base completion, a classifier may only have access to positive and unlabeled examples, where the unlabeled data consists of both positive and negative examples. One way that enables learnin…