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Noval Stream Data Mining Framework under the Background of Big Data

Wenquan Yi, Fei Teng, Jianfeng Xu
2016 Cybernetics and Information Technologies  
So, these traditional stream data mining methods need to be enhanced for big data applications. To resolve this issue, a hybrid framework is proposed for big steam data mining.  ...  However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data.  ...  Acknowledgements: Authors would like to thank the anonymous referees for their constructive comments and valuable suggestions.  ... 
doi:10.1515/cait-2016-0053 fatcat:fpqjhuan7jg23gjm2ivntaqvzq

Combining Stream Mining and Neural Networks for Short Term Delay Prediction [article]

Maciej Grzenda, Karolina Kwasiborska, Tomasz Zaremba
2018 arXiv   pre-print
The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons.  ...  The primary objective of the work is to propose short term hybrid delay prediction method.  ...  Acknowledgements This is a pre-print of a contribution published in In: Pérez Garca H., Alfonso-Cendón J., Sánchez González L., Quintián H., Corchado E.  ... 
arXiv:1706.05433v2 fatcat:utvkuhkinjdjzc6gesptvtdu5u

Online Active Learning Ensemble Framework for Drifted Data Streams

Jicheng Shan, Hang Zhang, Weike Liu, Qingbao Liu
2018 IEEE Transactions on Neural Networks and Learning Systems  
We present a new online active learning ensemble framework for drifting data streams based on a hybrid labeling strategy that includes the following: 1) an ensemble classifier, which consists of a long-term  ...  stable classifier and multiple dynamic classifiers (a multilevel sliding window model is used to create and update the dynamic classifiers to effectively process both the gradual drift type and sudden  ...  Brzezinski for sharing his scripts, which were used in [3] .  ... 
doi:10.1109/tnnls.2018.2844332 pmid:29994730 fatcat:3bhwcx5gnbfzddmpmupnwtynmy

Research on the Fastest Detection Method for Weak Trends under Noise Interference

Guang Li, Jing Liang, Caitong Yue
2021 Entropy  
It is based on calculating the data weight in each window by applying variable weights, while maintaining the method of trend-effective integration accumulation.  ...  The new algorithm changes the traditional calculation method of the trend anomaly detection score, which calculates the score in a short window.  ...  Jaccard's coefficient represents a similarity measure of the online stream classification algorithm for data stream machine learning.  ... 
doi:10.3390/e23081093 fatcat:cyn5g4i3a5fvridruccynbqcqm

Fuzzy Rank Based Parallel Online Feature Selection Method using Multiple Sliding Windows

B. Venkatesh, J. Anuradha
2021 Open Computer Science  
In this paper, we propose a parallel online feature selection method using multiple sliding-windows and fuzzy fast-mRMR feature selection analysis, which is used for selecting minimum redundant and maximum  ...  So, online streaming feature selection methods gained more attention but the existing methods had demerits like low classification accuracy, fails to avoid redundant and irrelevant features, and a higher  ...  OSFSW uses a sliding window method for selecting the features from the online streaming data and it needs two stages for selecting the features.  ... 
doi:10.1515/comp-2020-0169 fatcat:wyrlqgwnd5debcqhvxoanfhole

Contextual Arabic Handwriting Recognition System using Embedded Training based Hybrid HMM/MLP Models

Mouhcine Rabi, Mustapha Amrouch, Zouhir Mahani
2017 Transactions on Machine Learning and Artificial Intelligence  
Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large  ...  capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems.The proposed work reports an effective method on improvement our previous work that  ...  Three different HMMs are used: one for the vertical sliding window and two for slanted windows a fusion scheme is used to combine the three HMMs. [06] proposed a combined scheme for Arabic handwritten  ... 
doi:10.14738/tmlai.54.2983 fatcat:oiq5rik3hzevfknf4bpgzp4ll4

Two-Stage Cost-Sensitive Learning for Data Streams with Concept Drift and Class Imbalance

Yange Sun, Yi Sun, Honghua Dai
2020 IEEE Access  
Such methods are often implemented through sliding window technology (Sliding Window).  ...  Ensemble-based methods for data streams classification can be categorized into the following three types: block-based ensembles, online ensembles, and hybrid ensembles [35] . (1) Specifically, in block-based  ... 
doi:10.1109/access.2020.3031603 fatcat:jv3i5kdvsbefrettjrbjdhjwk4

Verifying and Mining Frequent Patterns from Large Windows over Data Streams

Barzan Mozafari, Hetal Thakkar, Carlo Zaniolo
2008 2008 IEEE 24th International Conference on Data Engineering  
Thus, we propose a frequent itemset mining method for sliding windows, which is faster than the state-of-the-art methods-in fact, its running time that is nearly constant with respect to the window size  ...  Mining frequent itemsets from data streams has proved to be very difficult because of computational complexity and the need for real-time response.  ...  We also propose a delta-maintenance based method that utilizes the fast verifier for incremental frequent itemset mining over very large sliding windows, called Sliding Window Incremental Miner(SWIM)(Section  ... 
doi:10.1109/icde.2008.4497426 dblp:conf/icde/MozafariTZ08 fatcat:lp3wreuaxvgwzb7tgw5nvzyehe

Stream Classification [chapter]

Jerzy Stefanowski, Dariusz Brzezinski
2017 Encyclopedia of Machine Learning and Data Mining  
An entry to appear in the Encyclopedia of Machine Learning (Springer) Definition Stream classification is a variant of incremental learning of classifiers that has to satisfy requirements specific for  ...  A data stream is a potentially unbounded, ordered sequence of data items, which arrive continuously at high-speeds.  ...  Finally, several detection methods use two subsets of the stream: a reference window and a sliding window of the most recent examples.  ... 
doi:10.1007/978-1-4899-7687-1_908 fatcat:stqhnfdiynhx3opz3aldpn2cdi

Improved HMM for Cursive Arabic Handwriing Recognition System using MLP Classifier

Mouhcine Rabi, Mustapha Amrouch, Zouhir Mahani
2017 Transactions on Machine Learning and Artificial Intelligence  
Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large  ...  capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems.The proposed work reports an effective method on improvement our previous work that  ...  window frame form one stream and its derivative features are part of the second stream.  ... 
doi:10.14738/tmlai.54.2969 fatcat:uvf3od45vrcs5l6oxfnd5iil6a

On utilizing weak estimators to achieve the online classification of data streams

Hanane Tavasoli, B. John Oommen, Anis Yazidi
2019 Engineering applications of artificial intelligence  
We propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties.  ...  A B S T R A C T Classification, typically, deals with unique and distinct training and testing phases. This paper pioneers the concept when these phases are not so clearly well-defined.  ...  These results indicated the uniform superiority of the SLWE-based classifier over the classification scheme using a MLEbased sliding window method.  ... 
doi:10.1016/j.engappai.2019.08.015 fatcat:lq3hith75ngc3ghkbgftrwokuu

FDiBC: A Novel Fraud Detection Method in Bank Club based on Sliding Time and Scores Window

Seyed M. H. Hasheminejad, Z. Salimi
2018 Journal of Artificial Intelligence and Data Mining  
In this paper, we propose a novel sliding time and scores window-based method, called FDiBC (Fraud Detection in Bank Club), to detect fraud in bank club.  ...  In FDiBC, firstly, based on each score obtained by customer members of bank club, 14 features are derived, then, based on all the scores of each customer member, five sliding time and scores window-based  ...  After deriving the features from table 2, for each feature vector, a label representing whether the sliding window-based feature vector is a fraud or a common one is provided.  ... 
doi:10.22044/jadm.2017.964 doaj:9741e69fd5ff487c9a91bbc5f1eb4bfb fatcat:mhaw5bxto5hvzdmhz7bflmbmtq

A concept-drift perspective on prototype selection and generation

Ludmila I. Kuncheva, Iain A. D. Gunn
2016 2016 International Joint Conference on Neural Networks (IJCNN)  
The new taxonomy can serve as a road-map for researching the intersection area and inform the development of new methods.  ...  We proceed to create a bespoke taxonomy of these methods and illustrate it with ten examples from the literature.  ...  point or a sliding window of the most recent points is available for learning [12] .  ... 
doi:10.1109/ijcnn.2016.7727175 dblp:conf/ijcnn/KunchevaG16 fatcat:o33oo5i6bzej7nn544cdplljvi

Real-time Public Mood Tracking of Chinese Microblog Streams with Complex Event Processing

Si Shi, Dawei Jin, Goh Tiong-Thye
2017 IEEE Access  
To address this issue, we propose a hierarchical framework for real-time public mood time series tracking over Chinese microblog streams using complex event processing.  ...  For the public mood time series, we use smoothing and trend following methods to find the rising or falling trends of the public mood.  ...  These methods can be grouped into the following three categories: knowledge based, machine learning based and hybrid.  ... 
doi:10.1109/access.2016.2633721 fatcat:hh7btht7sfcwdnc727pbay7yhu

Intelligent Detection Approaches for Spam

Guangchen Ruan, Ying Tan
2007 Third International Conference on Natural Computation (ICNC 2007)  
Exceeding margin update technique is also used for the dynamical update of each classifier in the window. A sliding window is employed for purge of out-of-date knowledge so far.  ...  This paper proposes intelligent detection approaches based on Incremental Support Vector Machine and Artificial Immune System for the spam of e-mail stream.  ...  The exceeding margin update technique is also used for dynamical update of each classifier in the window. A sliding window is employed for purge of out-of-date knowledge so far.  ... 
doi:10.1109/icnc.2007.448 dblp:conf/icnc/RuanT07 fatcat:p3thr63b5jaopd43olh32zbhfq
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