Centroid Neural Network with Pairwise Constraints for Semi-supervised Learning

Ngoc, Minh Tran; Park, Dong-Chul

doi:10.1007/s11063-018-9794-8

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Parameter is utilized to control the extent of label diffusion in Eq. (12). Figure 2 shows the performance curves with different values of in German (left) and Australian (right) credit card application data sets.…”

Section: Lists the Precision Tpr And Tnr Values Ofmentioning

confidence: 99%

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Semi-Supervised Clustering for Financial Risk Analysis

Han

Wang

2021

Neural Process Lett

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Many methods have been developed for financial risk analysis. In general, the conventional unsupervised approaches lack sufficient accuracy and semantics for the clustering, and the supervised approaches rely on large amount of training data for the classification. This paper explores the semi-supervised scheme for the financial data prediction, in which accurate predictions are expected with a small amount of labeled data. Due to lack of sufficient distinguishability in financial data, it is hard for the existing semi-supervised approaches to obtain satisfactory results. In order to improve the performance, we first convert the input labeled clues to the global prior probability, and propagate the'soft' prior probability to learn the posterior probability instead of directly propagating the'hard' labeled data. A label diffusion model is then constructed to adaptively fuse the information at feature space and label space, which makes the structures of data affinity and labeling more consistent. Experiments on two public real financial datasets validate the effectiveness of the proposed method.

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Section: Lists the Precision Tpr And Tnr Values Ofmentioning

confidence: 99%

“…As suggested in [10], a multiple criteria decision making strategy can better evaluate clustering algorithms in the domain of risk analysis. Typical fully-supervised methods include the machine learningbased methods [11,12]. Compared with unsupervised methods, fully-supervised methods can generally achieve higher prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Clustering for Financial Risk Analysis

Han

Wang

2021

Neural Process Lett

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“…1] is a highly expected feature in any framework. Thus, this research focuses on building a framework for data pre-processing and storage security for business-critical data [19].…”

Section: Introductionmentioning

confidence: 99%

An Automated Framework for Enterprise Financial Data Pre-processing and Secure Storage

Alamanda¹,

Pabboju²,

Narasimha³

2021

IJACSA

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The analysis on the financial data is highly crucial and critical as the results or the conclusion communicated based on the analysis can generate a greater impact on the personal and enterprise scale business processes. The primary source of the financial data is the business process and often the data is collected by automation tools deployed at various points of the business process data flow. The data entered in the business process is primary done by the stake holders of the process and at various levels of the process the data is modified, translated and sometimes completed transverter, due to which the impurities or anomalies are introduced in the data. These impurities, such as outliers and missing values, cause a high impact on the final decision after processing these datasets. Hence an appropriate pre-processing for financial data is the demand of the research. A good number of parallel research outcomes can be observed to solve these problems. Nonetheless, majority of the solutions are either highly time complex or not accurate effectively. Thus, this work proposes an automated framework for identification and imputation of the outliers using the iterative clustering method, identification and imputation of the missing values using Differential count based binary iterations method and finally the secure data storage using regression based key generation. The proposed framework has showcased nearly 100% accuracy in detection of outliers and missing values with highly improved time complexity.

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