Edge-based mining of frequent subgraphs from graph streams
In the current era of Big data, high volumes of valuable data can be generated at a high
velocity from high-varieties of data sources in various real-life applications ranging from …
velocity from high-varieties of data sources in various real-life applications ranging from …
[PDF][PDF] Parallel MCNN (PMCNN) with application to prototype selection on large and streaming data
VS Devi, L Meena - Journal of Artificial Intelligence and Soft …, 2017 - sciendo.com
Abstract The Modified Condensed Nearest Neighbour (MCNN) algorithm for prototype
selection is order-independent, unlike the Condensed Nearest Neighbour (CNN) algorithm …
selection is order-independent, unlike the Condensed Nearest Neighbour (CNN) algorithm …
Ensemble online classifier based on the one-class base classifiers for mining data streams
I Czarnowski, P Jędrzejowicz - Cybernetics and Systems, 2015 - Taylor & Francis
The problem addressed in this study concerns mining data streams with concept drift. The
goal of the article is to propose and validate a new approach to mining data streams with …
goal of the article is to propose and validate a new approach to mining data streams with …
Incremental weighted one-class classifier for mining stationary data streams
B Krawczyk, M Woźniak - Journal of Computational Science, 2015 - Elsevier
Big data analytics, especially data stream mining, is among the most popular contemporary
machine learning problems. More and more often real-life tasks could generate massive and …
machine learning problems. More and more often real-life tasks could generate massive and …
A hybrid heuristic algorithm for evolving models in simultaneous scenarios of classification and clustering
Abstract Machine Learning is currently an important research field that attracts interest due
to its importance for discovering hidden knowledge or patterns from big datasets. In this …
to its importance for discovering hidden knowledge or patterns from big datasets. In this …
An ensemble classification algorithm based on information entropy for data streams
J Wang, S Xu, B Duan, C Liu, J Liang - Neural Processing Letters, 2019 - Springer
Data stream mining has attracted much attention from scholars. In recent researches,
ensemble classification has been wide aplied in concept drift detection; however, most of …
ensemble classification has been wide aplied in concept drift detection; however, most of …
数据流集成分类算法综述.
许冠英， 韩萌， 王少峰， 贾涛 - Application Research of …, 2020 - search.ebscohost.com
数据流集成分类算法综述 Page 1 收稿日期:20180911;修回日期:20181026 基金项目:国家
自然科学基金资助项目(61563001);宁夏自然科学基金资助项目 (NZ17115);北方民族大学研究生 …
自然科学基金资助项目(61563001);宁夏自然科学基金资助项目 (NZ17115);北方民族大学研究生 …
Hybrid decision trees for data streams based on Incremental Flexible Naive Bayes prediction at leaf nodes
Mining data over streams in one pass and using constant memory is a challenging task.
Decision trees are one of the most popular classifiers for both batch and incremental …
Decision trees are one of the most popular classifiers for both batch and incremental …
A novel approach using incremental oversampling for data stream mining
N Anupama, S Jena - Evolving Systems, 2019 - Springer
Data stream mining is very popular in recent years with advanced electronic devices
generating continuous data streams. The performance of standard learning algorithms is …
generating continuous data streams. The performance of standard learning algorithms is …
Forming ensembles of soft one-class classifiers with weighted bagging
B Krawczyk - New Generation Computing, 2015 - Springer
For many real-life problems obtaining representative examples from a given class is
relatively easy, while for the remaining ones are difficult, or even impossible. However, we …
relatively easy, while for the remaining ones are difficult, or even impossible. However, we …