[PDF] Top 20 Classification and Novel Class Detection in Data Streams Using Strings
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Classification and Novel Class Detection in Data Streams Using Strings
... Data streams are difficult to process also because of the four main challenges posed by ...Since data stream is a fast and an ongoing process, it is assumed to be of infinite ...torical data ... See full document
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Role of big-data in classification and novel class detection in data streams
... “Data streams” is defined as class of data generated over “text, audio and video” channel in continuous ...The streams are of infinite length and may comprise of structured or ... See full document
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A Comparative study of Data stream classification using Decision tree and Novel class Detection Techniques
... mining novel Classes in data streams with base learner K-NN (K-nearest neighbor) and decision ...novelty detection is also non-parametric. Novelty detection is also closely related to ... See full document
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A SURVEY ON VARIOUS CLASSIFICATION AND NOVEL CLASS DETECTION APPROACHES FOR FEATURE EVOLVING DATA STREAM
... of data-stream classification technique is cluster based approach which addresses the problem of concept evolution in addition to infinite length and concept drift to detect novel classes in ... See full document
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Novel and Recurring Class Detection using Ensemble of Classifiers: A Class based Approach
... mining data streams. Mining data stream has become a crucial task due to its wide range of applications such as network in- trusion detection, credit card fraud identification, identifying ... See full document
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An Efficient Indexing Structure for Ensemble Classification of Data Streams Using Forest Tree Mechanism
... for data stream classification, as it facing problem to large size of stream data and concept ...critical data stream applications, e.g. Web traffic. In this data streams usually ... See full document
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AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition
... in data stream such as an online infor- mation network (OLIN) [Last, 2002] and a fuzzy classifier in [Lughofer and Angelov, ...of novel concept in the stream as in Figure 1(b). Detecting a novel ... See full document
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Outlier Detection and Analysis of Data Stream Classification Using Text Clustering
... topic detection and classification is most important in social data ...streaming data. To find outliers dynamically by using various clustering ...efficient data mining procedure ... See full document
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SURVEY OF CLASSIFICATION RULE MINING TECHNIQUES FOR IDENTIFYING DISEASE CAUSE AND DIAGNOSIS
... of data stream classification is the arrival of data in an abstractly immeasurable stream and the chance to determine each record is briefed ...stream classification algorithm executing in ... See full document
10
A Study of Feature Selection Methods in Intrusion Detection System: A Survey
... high-dimensional data or large- scale ...of data dimensionality, feature selection as a pre-processing step is becoming an essential part in building intrusion detection systems ...a novel ... See full document
9
Unsupervised classification of data streams based on typicality and eccentricity data analytics
... As possible solutions to the mentioned problem, one can mention the works of [8], [9] and [10]. Still, some of the presented approaches might not be considered as fully online, since they often require offline batch ... See full document
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Ensemble Dynamics in Non-stationary Data Stream Classification
... more data available in the form of data streams ...in data streams. Suppose a sensor net- work that produces data related to credit card transactions of a bank from different ... See full document
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On Expressiveness and Uncertainty Awareness in Rule-based Classification for Data Streams
... Mining data streams is a core element of Big Data ...Big Data, the other three being volume, variety and veracity. As data streams in, mod- els are constructed using ... See full document
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A Systematic Study of Online Class Imbalance Learning with Concept Drift
... the detection performance of the three active concept drift ...the class imbalance ...each class over time. Unlike the cases with class imbalance changes, where it is possible for the ... See full document
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... Here the data stream is divided into equal sized chunks and a classification model is trained from each chunk. This model replaces one of the existing models in the ensemble, keeping the ensemble size ... See full document
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Analytical Study of Data Mining on Data Stream Using Skewed Distribution of Data
... Evolved from numerous research communities, especially those from developed countries, the analytical engine within these solutions and software are driven by artificial parallel computing, econometrics, expert systems, ... See full document
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An Efficient Image Classification Using Class Imbalance In High-Dimensional Data
... Object recognition has been an active research focus in field of image processing. Using object models that are known a priori, an object recognition system finds objects in the real world from an image. This is ... See full document
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Fault Classification of Reciprocating Compressor Based on Neural Networks and Support Vector Machines
... the data vectors. Both data vectors and target vector were divided into two subsets of equal size by taking every other vector value, of which one was for training the SVM and the other for ... See full document
7
Epileptic Seizure Data Classification Using RBAs and Linear SVM
... data-analysis methods are all based on linear and stationary assumptions like Fourier transformation makes assumption of the signal period which creates spectral leakage. As is well known, the natural physical ... See full document
14
Performance Analysis of Classifying Unlabeled Data from Multiple Data Sources
... We have number of methods for estimating the accuracy a model like holdout method, random sub-sampling method, k- fold cross-validation method, leave-one-out method, bootstrap method etc. In this project, i use k-fold ... See full document
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