Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining [PDF]
Mathematical Problems in Engineering, 2013 Imperfect data stream leads to tree size explosion and detrimental accuracy problems. Overfitting problem and the imbalanced class distribution reduce the performance of the original decision-tree algorithm for stream mining. In this paper, we propose an incremental optimization mechanism to solve these problems.
Hang Yang, Simon Fong
openalex +4 more sources
A configurable board-level adaptive incremental diagnosis technique based on decision trees [PDF]
2015 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS), 2015 Functional diagnosis for complex electronic boards is a time-consuming task that requires big expertise to the diagnosis engineers. In this paper we propose a new engine for board-level adaptive incremental functional diagnosis based on decision trees. The engine incrementally selects the tests that have to be executed and based on the test outcomes it
Cristiana Bolchini, Luca Cassano
openalex +2 more sources
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
openalex +3 more sources
Decision Tree Incremental Learning Algorithm Oriented Intelligence Data [PDF]
International Journal of Performability Engineering, 2018 Hongbin Wang
openalex +2 more sources
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
doaj +3 more sources
Frontiers in Physics, 2023 This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments.
Naihua Ji +5 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm
Discrete Dynamics in Nature and Society, 2022 With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology.
Fen Yang
doaj +1 more source
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
doaj +1 more source