Fuzzy MLKNN in Credit User Portrait

Zhang, Zhuangyi; Han, Lu; Chen, Muzi

doi:10.3390/app122211342

Cited by 2 publications

(2 citation statements)

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“…Minghui You et al [10] proposed a behavior-aware user profiling technique that utilizes data mining of user attributes to construct an initial user portrait by identifying user behavior patterns through perception. Zhang et al [11] introduced an enhanced fuzzy MLKNN multi-label learning algorithm based on MLKNN, aiming to address the challenges of subjective augmentation caused by credit data discretization and the absence of multi-dimensional credit user portrait in current credit data research.…”

Section: Related Workmentioning

confidence: 99%

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Multidimensional Private Information Portrait in Social Network Users

Shan,

Wang,

Sun

2023

IJACSA

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In order to tackle the challenges of users' weak privacy awareness and frequent disclosure of private information in social network, this paper proposes a multidimensional privacy information portrait model of users in Chinese social networks. Because the TF-IDF (Term Frequency-Inverse Document Frequency, TF-IDF) algorithm does not consider the distribution of feature terms among and within classes, uses the TF-IDF algorithm based on the bag-of-words model to calculate the sensitivity of user privacy information. Considering the diversity of user privacy information, this paper proposes the PROLM (Positive reverse order lookaround matching ) algorithm, which is combined with the Flashtext+ (improved Flashtext) algorithm and SMA (string matching algorithm, SMA), the PROLM_FlashText+_SMA to extract user personal privacy information and location where the privacy information is located, and return the sensitivity. Using the BERT (Bidirectional Encoder Representation from Transformers, BERT)-Softmax privacy information classification model, the privacy information is classified into high, moderate and mild privacy information, and a multidimensional privacy information portrait of the user is constructed based on the privacy information and sensitivity. The experiments show that the accuracy of PROLM_FlashText+_SMA algorithm for privacy information extraction reaches 93.63%, and the overall F1 index of privacy information classification using the BERT-Softmax model reaches 0.9798 on the test set, better than baseline comparison model, has better privacy information classification effect.

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Section: Related Workmentioning

confidence: 99%

“…The probability of x belonging to all categories is calculated using Eq. (11), and the category with the highest probability is chosen as the classification category for x.…”

Section: ) Classificationmentioning

confidence: 99%