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A framework for classifier adaptation and its applications in concept detection

Jun Yang, Alexander G. Hauptmann
2008 Proceeding of the 1st ACM international conference on Multimedia information retrieval - MIR '08  
It directly modifies the decision function of an existing classifier of any type into a classifier for a new domain, based on limited labeled data in the new domain and no "old data", which makes it an  ...  This paper describes a generic framework for function-level classifier adaptation based on regularized loss minimization.  ...  This work was supported in part by the National Science Foundation (NSF) under Grants No. IIS-0535056 and CNS-0751185.  ... 
doi:10.1145/1460096.1460171 dblp:conf/mir/YangH08 fatcat:jpdxiq473fhbffdu4avicwbahy

Analyzing Performance of Classification Algorithms on Concept Drifted Data Streams

Aradhana Nyati, Divya Bhatnagar, Avinash Panwar
2017 International Journal of Computer Applications  
Mobile devices, streaming, remote sensing applications which are networked digital information systems, encounter the issue of the size of data and the capacity to be adaptive to changes in concept in  ...  In this paper the main issue of concept drift is addressed with real and synthetic data streams and the comparison of ensemble classifiers has been made in view of concept drift for the assessment of the  ...  Gama et al. defined adaptive learning process and categorization for handling concept drift and presented a set of illustrative applications [2] .  ... 
doi:10.5120/ijca2017913065 fatcat:u7yda2myobcxdjvgkxxfztcqb4

Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Based Healthcare Applications

Dipti Durgesh Patil
2012 Advanced Computing An International Journal  
This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a significant summary of existing approaches  ...  These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health  ...  The framework is innovative as it dynamically adapts to the changes happened in the vital signals and updates the model for health risk predictions.  ... 
doi:10.5121/acij.2012.3408 fatcat:4qsuijdiebfblpt22b3r6bmnii

Review paper on adapting data stream mining concept drift using ensemble classifier approach

Nilima Motghare, Arvind Mewada
2014 IOSR Journal of Computer Engineering  
Data stream is massive, fast changing and infinite in nature. It is very natural that large amount of  ...  Ensemble classifier creates and removes base algorithm in response to change in performance, which makes it well suited for problem of concept drift.  ...  It uses weighted majority and adaptive sliding window approaches to handle noisy and concept drifting data streams for improving performance of classification in terms of accuracy.  ... 
doi:10.9790/0661-1654120123 fatcat:wyub5nwbgbfzpgwo5x3hbxwski

Introduction to the special issue on handling concept drift in adaptive information systems

Mykola Pechenizkiy, Indre Zliobaite
2012 Evolving Systems  
We would also like to thank the editorial office of the Evolving Systems journal for their support.  ...  results has received funding from the European Commission within the Marie Curie Indus-try and Academia Partnerships and Pathways (IAPP) programme under Grant Agreement No. 251617.  ...  The third paper ''EVE-A Framework for Event Detection'' by Iris Adä and Michael Berthold introduces a generic framework for event detection, where events can include outliers, model changes and data drifts  ... 
doi:10.1007/s12530-012-9070-5 fatcat:lsbljs2wmvftzdtc4qe7tg4mbm

Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing [article]

Shujian Yu, Zubin Abraham, Heng Wang, Mohak Shah, Yantao Wei and José C. Príncipe
2019 arXiv   pre-print
In this paper, we first present a hierarchical hypothesis testing (HHT) framework that can detect and also adapt to various concept drift types (e.g., recurrent or irregular, gradual or abrupt), even in  ...  A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a phenomenon also known as  ...  testing (HHT) framework for concept drift detection and adaptation.  ... 
arXiv:1707.07821v6 fatcat:dwpdkkbd7ffvdov4fdrjuzuh54

Ensemble Dynamics in Non-stationary Data Stream Classification [chapter]

Hossein Ghomeshi, Mohamed Medhat Gaber, Yevgeniya Kovalchuk
2018 Studies in Big Data  
Since each application has its own characteristics and conditions, it is difficult to introduce a single approach that would be suitable for all problem domains.  ...  This phenomenon is called concept drift. Ensemble learning techniques have been proven effective adapting to concept drifts.  ...  In this mechanism, when a classifier becomes 'unhelpful' in a new concept, it is considered as an obsolete classifier and is removed from the ensemble. • Drift detection based: when a concept drift detection  ... 
doi:10.1007/978-3-319-89803-2_6 fatcat:od6ibg7bnrgzregyd7np6wud4e

EventMapper: Detecting Real-World Physical Events Using Corroborative and Probabilistic Sources [article]

Abhijit Suprem, Calton Pu
2020 arXiv   pre-print
The ubiquity of social media makes it a rich source for physical event detection, such as disasters, and as a potential resource for crisis management resource allocation.  ...  We describe three applications built on EventMapper for landslide, wildfire, and flooding detection.  ...  In this paper, we present EventMapper , a framework for event recognition that exploits the co-dependence of data processing and event detection to support long-term event recognition that can adapt to  ... 
arXiv:2001.08700v1 fatcat:4bam7ha7qbbi7durwrjclt6s2q

Adaptive Mobile Malware Detection Model Based on CBR

Kyaw Soe Moe, Mya Mya Thwe
2019 Zenodo  
An adaptive malware detection approach is proposed based on case based reasoning technique in this paper to handle the concept drift issue in mobile malware detection.  ...  Thus, an adaptive malware detection approach is required to effectively detect the concept drift of mobile malware and maintain the accuracy.  ...  Th concept drift in the context of machine learning and it becomes one of the most challenging issues for mobile malware detections.  ... 
doi:10.5281/zenodo.3587746 fatcat:qkqgdenvgjdelkkwfom5a3xy2u

Adaptation of Word Vectors using Tree Structure for Visual Semantics

Nakamasa Inoue, Koichi Shinoda
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
Adapted word vectors are obtained for each word by taking a weighted sum of a given original word vector and its hypernym word vectors.  ...  We propose a framework of word-vector adaptation, which makes vectors of visually similar concepts close to each other.  ...  In contrast, our framework of word-vector adaptation to visual applications aims to make word vectors of visually similar concepts close to each other without using metadata.  ... 
doi:10.1145/2964284.2967226 dblp:conf/mm/InoueS16 fatcat:nyr5t7zzxfglrlfar52a7lmsim

Design Pattern Classifiers under Attack for Security Evaluation using Multimodal System

Rupali Baliram Navalkar, Prof. Rajeshri R Shelke
2017 International Journal of Trend in Scientific Research and Development  
Here, propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in real applications.  ...  This framework can be applied to different classifiers on one of the application from the spam filtering, biometric authentication and network intrusion detection.  ...  Framework In this work we create a new framework to provide security to adversarial application. we address issues developing a framework for the empirical evaluation of classifier security also propose  ... 
doi:10.31142/ijtsrd97 fatcat:monqkmon4rdldlqjhdglayk4mu

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

Yange Sun, Yi Sun, Honghua Dai
2020 IEEE Access  
Moreover, a window adaptation and drift detection mechanism, which guarantees that an ensemble can adapt promptly to concept drift, is embedded in our method.  ...  We propose a novel two-stage cost-sensitive framework for data stream classification by utilizing cost information in both feature selection stage and classification stage.  ...  It detects mutation concept drift by monitoring the error rate of classifiers, and builds a base classifier on the data block to cope the gradual concept drift.  ... 
doi:10.1109/access.2020.3031603 fatcat:jv3i5kdvsbefrettjrbjdhjwk4


Abhijit Suprem, Calton Pu
2019 Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems - DEBS '19  
To address this problem, we develop the ASSED (Adaptive Social Sensor Event Detection) framework with an ML-based event processing engine and show how it can perform simple and complex physical event detection  ...  We demonstrate ASSED capabilities through a landslide detection application that detects almost 350% more landslides compared to static approaches.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding  ... 
doi:10.1145/3328905.3329510 dblp:conf/debs/SupremP19 fatcat:yogwfx4hvjh2vksbfxupxdvfpe

Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams

Ali Pesaranghader, Herna Viktor, Eric Paquet
2018 Machine Learning  
The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators  ...  In addition, a number of methods have been developed to detect concept drifts in these streams.  ...  Finally, we wish to thank the anonymous reviewers for their invaluable feedback, that led us to improve this paper considerably.  ... 
doi:10.1007/s10994-018-5719-z fatcat:lkeartys4nfo3ko7epdwaxlksy

Self-tuning query mesh for adaptive multi-route query processing

Rimma V. Nehme, Elke A. Rundensteiner, Elisa Bertino
2009 Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology - EDBT '09  
ST-QM addresses adaptive query processing by abstracting it as a concept drift problem -a wellknown subject in machine learning.  ...  For such applications, it can be fruitful to eliminate the commonly made single execution plan assumption and instead execute a query using several plans, each optimally serving a subset of data with particular  ...  If a virtual concept drift is detected, first a new classifier C' for the new training set T' is computed.  ... 
doi:10.1145/1516360.1516452 dblp:conf/edbt/NehmeRB09 fatcat:md2ivbw2vbe47kdk537e2fuwxe
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