Classification Using Streaming Random Forests

Abdulsalam, Hanady M.; Skillicorn, David B.; Martin, Patrick

doi:10.1109/tkde.2010.36

Cited by 108 publications

(61 citation statements)

References 24 publications

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“…Most of the conventional learning techniques assume that there is a static dataset generated by an unknown yet stationary probability distribution, which can be stored and analyzed in multiple steps. Nevertheless, none of the latter assumptions are verifiable in several streaming scenarios and the development of new learners must account for several constraints [1,2,10,21,22,30,33]:…”

Section: Learning From Data Streamsmentioning

confidence: 99%

See 1 more Smart Citation

On Dynamic Feature Weighting for Feature Drifting Data Streams

Barddal

Gomes

Enembreck

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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Section: Learning From Data Streamsmentioning

confidence: 99%

“…Nonetheless, none of the latter assumptions can be verified in the streaming scenario and the development of algorithms must account for several constraints [2,21,33]. Firstly, instances arrive continuously over time and there is no control over the order that they arrive nor how they should be processed.…”

Section: Concept Driftmentioning

confidence: 99%

On Dynamic Feature Weighting for Feature Drifting Data Streams

Barddal

Gomes

Enembreck

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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“…At first, there is low error association and therefore high variety amongst component classifiers, which tends to elevated categorization correctness. The author in [12] measured the crisis of data stream categorization, where the data appear in a theoretically neverending stream, and the chance to scrutinize each record is concise.…”

Section: Literature Reviewmentioning

confidence: 99%

Bucketization based Flow Classification Algorithm for Data Stream Privacy Mining

Kesavaraj¹,

Sukumaran²

2013

IJCA

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In recent years, data mining plays a major role in maintaining the huge volume of data from which it can derive the useful information. With the huge number of formation of data, the data wants to be lectured in a limit to the charge of growth. But it is complex to get over the set of meaningful information from the continuous set of data. Data-stream mining is a method which can discover important information from a huge contract of prehistoric data. For identification of useful information, the classification of continuous data streams is done. Current approaches in classifying the data streams are processed using supervised learning algorithms, which can be qualified with tagged data. Usually, manual classification of data is both expensive and time consuming. As a result, where massive amount of data emerge at a high speed, tagged data might be very sparse. Therefore, only a restricted amount of training data might be accessible for constructing the classification models, tend to badly trained classifiers. To overcome the issue, in this work, a novel technique is presented to build a classification set having both unlabeled and a small amount of labeled instances. This model is built by using the Flow Classification Algorithm (FCA). The FC algorithm is able to judge internally on set of marked data. Before classification, the correlation set of attributes in the each record set are grouped using bucketization technique. The superiority of models updated from them is enough for utilization of unlabeled records, or whether more set of labeled records are needed for classification is processed. Experimental evalaution is conducted to the proposed FC technqiue over its counterparts to find a set of diverse solution in terms of execution time, classification accuracy and security. Performance metrics for evaluation of proposed FCA technique shows that the security level is 10-15% high against existing work.

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“…In this approach, required features from HR and spo2 signals are extracted and applied to two different classifications and clustering algorithms such as K means and classification using random forests [4], and checking the efficiency of both of them. As the signals are continuous in nature and processed in real time the algorithms to be used are data stream mining algorithms.…”

Section: Related Workmentioning

confidence: 99%

APNEA Detection on Smart Phone

Patil¹,

Wadhai²,

Gujar³

et al. 2012

IJCA

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A flexible framework that performs real-time analysis of physiological data to monitor people's health conditions is discussed in this paper. Patients suspected of suffering sleep apnea and hypopnea syndrome (SAHS) have to undergo sleep studies such as expensive polysomnography to be diagnosed. Healthcare professionals are constantly looking for ways to improve the ease of diagnosis and comfort for this kind of patients as well as reducing both the number of sleep studies they need to undergo and the waiting times. Relating to this scenario, some research proposals and commercial products are appearing, but all of them record the physiological data of patients to portable devices and, in the morning, these data are loaded into hospital computers where physicians analyze them by making use of specialized software. The aim of this paper is to show a very accurate classifier that is able of identifying the presence of sleep apneas from blood oxygen saturation signal fragments taken from pulsioximetry systems (SpO2 & HRV) implemented on smart phone in real time.

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Classification Using Streaming Random Forests

Cited by 108 publications

References 24 publications

On Dynamic Feature Weighting for Feature Drifting Data Streams

On Dynamic Feature Weighting for Feature Drifting Data Streams

Bucketization based Flow Classification Algorithm for Data Stream Privacy Mining

APNEA Detection on Smart Phone

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