A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a Distributed Wireless Sensor Network

Yang, Hang; Fong, Simon; Sun, Guangmin; Wong, Raymond K.

doi:10.1155/2012/863545

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…Data preprocessing involves data transformation, data cleaning, and often feature selection for reducing the feature space for enhanced recognition accuracy; our previous papers have addressed in length this task for distributed wireless sensor networks [9,10]. We focus in this paper on the tasks of model induction and rule extraction, specifically by using data streaming algorithms and lightweight feature selection scheme suitable for high-speed incremental machine-learning, for gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

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

Fong¹,

Liang²,

Fister³

et al. 2015

Journal of Sensors

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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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Section: Introductionmentioning

confidence: 99%

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

Fong¹,

Liang²,

Fister³

et al. 2015

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The traditional methods of actual classification schemes [11][12][13], usually consist of the following basic parts as shown in The goal of the three steps to reduce the data explosion that is derived from real life data.…”

Section: Methodsmentioning

confidence: 99%

A novel approach to decision making of Mined Data using Dynamic Snapshot Pattern Recognition Algorithm (DS-PRA)

Iskandarani

2014

TMLAI

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A new approach to pattern recognition and decision machines profiling is proposed, proved and tested. The technique adopts the Snapshot method dynamically as a function of both organization policy and the organization department's policies. Such policies are associated with individual products and services provided by the organization with departments policies derived from general organization profile with the organization policy being a function of the various department's profiles. It is proved through real data the ability of such algorithm to classify, detect and predict policy changes and identify differences between different organizations. Also, such algorithm combines the concepts of general Artificial intelligence through the use of knowledge bases and Neural Networks by utilizing a similar weights matrix.

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“…Big data refers to the amount of data involved is huge to not pass the current mainstream software tools, achieve the collection, management, processing and finishing helping business decision-making becomes the purpose of consultation within a reasonable time [2]. Big data not only refers to the number of large amount of data volume (volumes), the initial measurement unit of big data is at least P (1000 T), E (1 million T) or Z (1 billion T), compared with the well-known G, the volume is not big.…”

Section: Figure 1 Hadoop Cluster Deployment Diagrammentioning

confidence: 99%

Research on Industry Data Analysis Model Based on Hadoop Big Data Platform

Xu¹,

Fan²,

Li³

2017

Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)

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Abstract. Big data analysis refers to the huge size of data analysis, from a large amount of data to extract potential valuable information. Hadoop, the core of the Hadoop distributed file system and MapReduce, provides users with the underlying, detailed, transparent distributed infrastructure. In this paper, a business model statistic system based on big data is analyzed. This paper describes the traditional industry data analysis model based on big data technology, and puts forward the existing problems. The paper presents research on industry data analysis model based on Hadoop big data platform.

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A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a Distributed Wireless Sensor Network

Cited by 20 publications

References 8 publications

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

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

A novel approach to decision making of Mined Data using Dynamic Snapshot Pattern Recognition Algorithm (DS-PRA)

Research on Industry Data Analysis Model Based on Hadoop Big Data Platform

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