An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams [PDF]
, 2007 One of most important algorithms for mining data streams is VFDT. It uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. Gama et al. have extended VFDT in two directions. Their system VFDTc can deal with continuous data and use more powerful classification techniques at tree leaves.
Tao Wang +3 more
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Cost-effectiveness of nirmatrelvir/ritonavir in COVID-19 patient groups at high risk for progression to severe COVID-19 in the Netherlands [PDF]
Cost Effectiveness and Resource AllocationBackground Nirmatrelvir/ritonavir is indicated for the treatment of COVID-19 in symptomatic adults with increased risk for severe illness, not requiring supplemental oxygen yet. From a Dutch societal perspective, a cost-utility assessment of nirmatrelvir/
Carlos H. Arteaga Duarte +4 more
doaj +2 more sources
Automated Screening for Three Inborn Metabolic Disorders: A Pilot Study [PDF]
Online Journal of Health & Allied Sciences, 2006 Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling ...
Kavitha S, Sarbadhikari SN, Ananth N Rao
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Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner [PDF]
European Conference on Machine Learning, 2001 Kurt Driessens +2 more
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Cost-effectiveness analysis of nirsevimab for prevention of respiratory syncytial virus disease among infants in Shanghai, China: A modeling study [PDF]
Human Vaccines & ImmunotherapeuticsChinese authority approved nirsevimab to prevent respiratory syncytial virus (RSV) in January 2024. We aimed to assess the cost-effectiveness of nirsevimab immunization among infants in Shanghai.
Qiang Wang +10 more
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Decision Tree Incremental Learning Algorithm Oriented Intelligence Data [PDF]
International Journal of Performability Engineering, 2018 Hongbin Wang
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IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES [PDF]
Applied Computer Science, 2021 Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to
Pannangi NARESH, R. SUGUNA
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Dynamic Weights Based Risk Rule Generation Algorithm for Incremental Data of Customs Declarations
Information, 2023 Aimed at shortcomings, such as fewer risk rules for assisting decision-making in customs entry inspection scenarios and relying on expert experience generation, a dynamic weight assignment method based on the attributes of customs declaration data and an
Ding Han +3 more
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Parallel Incremental Mining of Regular-Frequent Patterns from WSNs Big Data [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response.
Sadegh Rahmani-Boldaji +2 more
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Machine Learning Methods with Decision Forests for Parkinson’s Detection
Applied Sciences, 2021 Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data.
Moumita Pramanik +4 more
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