Design of Distributed Multidimensional Big Data Classification System Based on Differential Equation

Pei-ying, Wang

doi:10.1007/978-3-030-67871-5_35

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The reported results of both methods LC-KNN, and RC-KNN showed better performance when tested on Big datasets. It's also worth highlighting other Big Data classification-related studies, like as [77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,105,108,93,109,110,111,112,113,114,115,114,116,117,118,119,120,121,122,123,124,…”

Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that is based on the well-known magnetic force. Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space. Unknown examples are classified using the magnetic forces recorded in the trained model by various magnets/classes. When compared to existing classifiers, the proposed MF classifier achieves comparable classification accuracy, according to the experimental results utilizing 28 different datasets. More importantly, we found that the proposed MF classifier is significantly faster than all other classifiers tested, particularly when applied to Big datasets and hence could be a viable option for structured Big data classification with some optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

View full text Add to dashboard Cite

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“…Other Big Data classification-related works are also worth mentioning, such as [44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60] etc.…”

Section: Literature Reviewmentioning

confidence: 99%

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Tarawneh,

Alamri,

Al-Saedi

et al. 2023

IEEE Access

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Big data classification is a challenging task because most known classification methods need a long time and a lot of processing resources to execute such a task and use the vast amount of available data. In this paper, we propose a novel big data classification method that leverages the power of the KNN classifier and the efficiency of the ensemble learning technique to create a new method capable of performing classification tasks on big data efficiently. The proposed method picks tiny data chunks at random from a big dataset, with each chunk including random examples of a small number of randomly selected features. A weak KNN classifier is employed on each data chunk to perform classification on new (unseen) data, and the majority voting rule is used to reach the final classification decision based on the outcomes of the weak classifiers. The proposed method has a constant classification time, according to the time complexity analysis. Furthermore, the proposed method was found to be more efficient on a single node than existing methods, some of which run on a large cluster of nodes. Because of its speed and enhanced performance, the proposed method can be considered an ideal classifier for handling complex data types such as Geospatial data, Big trajectory data, and Big Data in general. INDEX TERMSBig data; Geospatial data; Trajectory data; Classification; Ensemble learning; KNN I. INTRODUCTIONFor decades, researchers have been studying and evaluating big data methodologies and tools. This interest stems from the massive volume of data exchanged and saved by social media users, medical organizations, educational institutions, and others.According to statistical reports, the number of users on different social media platforms has reached more than 2 billion [1]. WhatsApp, for example, has over 600 million users, more than half a billion photos, and one hundred million videos transferred and shared between users on a daily basis.[2]. Also, due to the huge advance in smartphone technology, it has become easier for users to share text\images and write posts on such social media platforms. Some reports show that the number of posts on Twitter in 2007 was 5K, this number became around 500 million after about 6 years, in 2013 [3], which indicates the massive amount of available data on social media in general. This amount of data is not restricted to social media, as many other platforms generate and store huge data volumes [4,5,6,7].This amount of data needs to be processed and analyzed in order to use it for building useful knowledge discovery and machine learning big data-based applications, like facial big data applications [8,9,10], signal big data [11] and various industry big data-based applications [12,13,14].Volume (big), Variety, and Velocity are among the most distinguishing characteristics of Big data, and as a result, it is attractive to have an efficient classification/prediction system to learn from such Big data. Such applications include, but are not limited to, medical [15,16], financial [17,18,19]...

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Design of Distributed Multidimensional Big Data Classification System Based on Differential Equation

Cited by 2 publications

References 7 publications

Magnetic Force Classifier: A Novel Method for Big Data Classification

Magnetic Force Classifier: A Novel Method for Big Data Classification

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

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