Effective feature representation using symbolic approach for classification and clustering of big data

Lavanya, P. G.; Kouser, K.; Suresha, M

doi:10.1016/j.eswa.2021.114658

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…Three popular ML algorithms were used to measure the accuracy of the detection model, specifically the Bayesian network, Logistic Regression, and Random Forest methods. These are simple but efficient algorithms (Hooda et al 2018 , 2020 ; Lavanya et al 2021 ; Lucas et al 2020 ). We propose an approach with a two-phase design to investigate the significance of the identified fraud-related variables.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Detection of price manipulation fraud through rational choice theory: evidence for the retail industry in Taiwan

Kuo

Tsang

2022

Secur J

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All enterprises, regardless of industry, are exposed to fraud risk. In the retail industry, the perpetrators of fraud manipulate the sales price of products instead of the quantity sold to avoid inventory discrepancies. Fraud examiners attempt to identify anomalous transactions using data analysis. This study analyzes the causes of fraudulent behavior, conceptualized based on the aspect of rational choice, and proposes an anomalous transaction detection model using variables identified as indicating fraud. A two-phase experiment analyzing a real-world data set from a retailer in Taiwan was designed to evaluate the performance of fraud variables. The findings demonstrate that these variables could slightly improve the results of the analysis and demonstrate that machine learning is applicable to fraud detection. In addition, this study contributes to rational choice theory to validate the applicability in fraud detection. Fraud examiners in the retail industry could reduce fraud losses by adopting the proposed approach, implemented using Weka software, to identify anomalies.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Detection of price manipulation fraud through rational choice theory: evidence for the retail industry in Taiwan

Kuo

Tsang

2022

Secur J

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“…In order to study the feature clusters of learning behaviors, it is necessary to adopt appropriate methods to achieve effective clustering (Winne, 2020). Since the second aspect needs to mine learning behaviors from massive data, unsupervised learning method is needed (Lavanya et al, 2021; Xia & Qi, 2023), Data analysis is to explore the possible rules through fully learning the unlabeled samples. Generally, the samples are divided into several relatively independent subsets by feature clustering.…”

Section: Related Workmentioning

confidence: 99%

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

Wang

Xia

2023

Sage Open

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The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

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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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Effective feature representation using symbolic approach for classification and clustering of big data

Cited by 8 publications

References 39 publications

Detection of price manipulation fraud through rational choice theory: evidence for the retail industry in Taiwan

Detection of price manipulation fraud through rational choice theory: evidence for the retail industry in Taiwan

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

Magnetic Force Classifier: A Novel Method for Big Data Classification

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