scholarly journals A review on data stream classification approaches

2016 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Sajad Homayoun ◽  
Marzieh Ahmadzadeh
Keyword(s):  
Active Learning ◽  
Data Stream ◽  
Frequent Pattern ◽  
Sensor Data ◽  
Data Stream Mining ◽  
Special Focus ◽  
Stream Mining ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Wide Range

<p>Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all instances to be labeled for creating classifier models, but there are some methods (Semi-Supervised Learning and Active Learning) in which unlabeled data is employed as well as labeled data. In this paper, by focusing on ensemble methods, semi-supervised and active learning, a review on some state of the art researches is given.</p>

Data Stream Mining Using Ensemble Classifier

2017 ◽  
pp. 236-249
Author(s):  
Snehlata Sewakdas Dongre ◽  
Latesh G. Malik
Keyword(s):  
Collaborative Filtering ◽  
Data Stream ◽  
Concept Drift ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Data Stream Mining ◽  
Main Concern ◽  
Stream Mining ◽  
Stream Classification ◽  
Data Stream Classification

A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly. Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play important role here how the different classifiers work collaborative within the ensemble to achieve a goal.

scholarly journals COLLABORATIVE DATA STREAM MINING IN UBIQUITOUS ENVIRONMENTS USING DYNAMIC CLASSIFIER SELECTION

2013 ◽  
Vol 12 (06) ◽  
pp. 1287-1308 ◽  
Author(s):  
JOÃO BÁRTOLO GOMES ◽  
MOHAMED MEDHAT GABER ◽  
PEDRO A. C. SOUSA ◽  
ERNESTINA MENASALVAS
Keyword(s):  
Classification Accuracy ◽  
Data Stream ◽  
Concept Drift ◽  
Feature Space ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Stream Classification ◽  
Classifier Selection ◽  
Local Accuracy ◽  
Real World Datasets

In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.

scholarly journals Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Simon Fong ◽  
Justin Liang ◽  
Iztok Fister ◽  
Iztok Fister ◽  
Sabah Mohammed
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Real Time ◽  
Gesture Recognition ◽  
Data Stream ◽  
Human Motion ◽  
Sensor Data ◽  
Data Stream Mining ◽  
Motion Sensing ◽  
Stream Mining

Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.

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