Fraud detection for job placement using hierarchical clusters-based deep neural networks

Kim, Jeongrae; Kim, Han Joon; Kim, Hyoungrae

doi:10.1007/s10489-019-01419-2

Cited by 34 publications

(24 citation statements)

References 68 publications

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“…For the purpose of evalauting the latter of the three, we extract text related information from Vidros et al's Fake JobPosting Prediction dataset (Vidros et al, 2017), and from PromptCloud's job dataset (PromptCloud, 2017). Both are high quality datasets listing full descriptions of jobs with large varieties, and versions of both datasets have been used by a plethora of publications (Balachander and Moh, 2018;Kim et al, 2019b;Alghamdi et al, 2019;Mahbub and Pardede, 2018;Reddy et al, 2018). For our purposes, we extract text data from the real job postings in Vidros et al's dataset. We also desire our evaluation set to have exposure to e-commerce applications, medical record information, and fake news articles.…”

Section: Datasetmentioning

confidence: 99%

Conical Classification For Efficient One-Class Topic Determination

Khanna¹

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute. Dataset Model Accuracy Balanced Accuracy Precision Recall F1 Score Time (s) ECommerce Linear OCSVM 0.900 ± 0.001 0.755 ± 0.003 0.978 ± 0.002 0.512 ± 0.007 0.672 ± 0.006 183.255 ± 3.821 RBF OCSVM 0.899 ± 0.001 0.753 ± 0.004 0.981 ± 0.003 0.508 ± 0.008 0.669 ± 0.007 201.339 ± 3.792 Sigmoid OCSVM 0.899 ± 0.002 0.752 ± 0.005 0.979 ± 0.003 0.508 ± 0.011 0.668 ± 0.009 193.402 ± 7.382 Poly OCSVM 0.885 ± 0.006 0.712 ± 0.002 0.995 ± 0.001 0.424 ± 0.003 0.595 ± 0.003 183.813 ± 2.639 IsolFor 0.199 ± 0.000 0.500 ± 0.000 0.199 ± 0.000 1.000 ± 0.000 0.333 ± 0.000 280.893 ± 12.543 OCRF 0.953 ± 0.000 0.947 ± 0.0176 0.800 ± 0.000 0.898 ± 0.036 0.889 ± 0.000 189.252 ± 244.041 PNB 0.638 ± 0.202 0.762 ± 0.092 0.786 ± 0.203 0.757 ± 0.305 0.687 ± 0.153 111.839 ± 59.581 GloVe CVDD 0.948 ± 0.017 0.906 ± 0.026 0.951 ± 0.015 0.983 ± 0.007 0.967 ± 0.011 188.462 ± 12.773 BERT CVDD 0.951 ± 0.128 0.910 ± 0.013 0.954 ± 0.017 0.987 ± 0.012 0.973 ± 0.028 233.626 ± 23.796 CC 0.988 ± 0.009 0.988 ± 0.007 0.956 ± 0.005 0.988 ± 0.009 0.971 ± 0.002 10.878 ± 0.036 FakeNews Linear OCSVM 0.818 ± 0.039 0.685 ± 0.082 0.819 ± 0.157 0.430 ± 0.232 0.495 ± 0.175 496.206 ± 5.353 RBF OCSVM 0.813 ± 0.033 0.706 ± 0.065 0.772 ± 0.184 0.500 ± 0.229 0.538 ± 0.086 533.529 ± 11.002 Sigmoid OCSVM 0.760 ± 0.046 0.648 ± 0.091 0.757 ± 0.259 0.436 ± 0.347 0.389 ± 0.166 514.913 ± 19.340 Poly OCSVM 0.850 ± 0.014 0.722 ± 0.053 0.846 ± 0.071 0.476 ± 0.128 0.592 ± 0.091 470.485 ± 2.950 IsolFor 0.238 ± 0.000 0.500 ± 0.000 0.238 ± 0.000 1.000 ± 0.000 0.384 ± 0.000 278.308 ± 5.449 OCRF 0.930 ± 0.000 0.955 ± 0.000 0.761 ± 0.000 1.000 ± 0.000 0.864 ± 0.000 197.232 ± 255.352 PNB 0.624 ± 0.223 0.773 ± 0.097 0.900 ± 0.142 0.697 ± 0.283 0.729 ± 0.152 218.296 ± 38.515 GloVe CVDD 0.906 ± 0.003 0.881 ± 0.011 0.938 ± 0.018 0.936 ± 0.022 0.936 ± 0.002 282.293 ± 31.88 BERT CVDD 0.899 ± 0.121 0.878 ± 0.218 0.923 ± 0.342 0.933 ± 0.231 0.927 ± 0.153 322.513 ± 49.659 CC 0.985 ± 0.005 0.985 ± 0.004 0.955 ± 0.002 0.987 ± 0.006 0.969 ± 0.001 13.952 ± 0.026 Jobs Linear OCSVM 0.913 ± 0.001 0.768 ± 0.004 0.971 ± 0.002 0.540 ± 0.009 0.694 ± 0.007 368.263 ± 6.227 RBF OCSVM 0.91...

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Section: Datasetmentioning

confidence: 99%

Conical Classification For Efficient One-Class Topic Determination

Khanna¹

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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“…In this context, data mining tasks, such as classification, clustering, applying association rules, and using neural networks, are employed [2]. In addition, AI is employed to build systems for fraud detection, such as classification-based systems [19,6,7,8], clustering-based systems [17,20,21], neural network-based systems [18,22,23] and support vector machine-based systems [9]. The techniques employed to construct credit card fraud detection systems using AI can be categorized into four main groups.…”

Section: B Groups Of Ai-based Techniquesmentioning

confidence: 99%

“…The authors in [21] tried to evaluate the detection problem by extracting the general pattern of the dataset to represent the fraud. In other words, the enhancement of the clustering methods relies only on the clusters used; this technique is called general enhancement.…”

Section: ) Clustering-based Systemsmentioning

confidence: 99%

Fraud Detection in Credit Cards using Logistic Regression

Alenzi¹,

Aljehane²

2020

IJACSA

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Due to the increasing number of customers as well as the increasing number of companies that use credit cards for ending financial transactions, the number of fraud cases has increased dramatically. Dealing with noisy and imbalanced data, as well as with outliers, has accentuated this problem. In this work, fraud detection using artificial intelligence is proposed. The proposed system uses logistic regression to build the classifier to prevent frauds in credit card transactions. To handle dirty data and to ensure a high degree of detection accuracy, a pre-processing step is used. The pre-processing step uses two novel main methods to clean the data: the mean-based method and the clustering-based method. Compared to two well-known classifiers, the support vector machine classifier and voting classifier, the proposed classifier shows better results in terms of accuracy, sensitivity, and error rate.

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“…Although powerful to capture non-linear relationships in this dimension reduction mapping, its use may reduce the model results explainability. This limitation was tackled by using hierarchical results of a clustering method applied to the autoencoder output to better identify and explain different types of fraud [Kim et al 2019].…”

Section: Work On Fraud Detectionmentioning

confidence: 99%

Spending Segmentation and Outlier Detection in Brazilian Elections

Simoes¹,

Verri²,

Yoneyama³

2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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The political campaigns in Brazilian elections are mostly financed by public money. Every candidate has to provide detailed accountability reports to the legal authorities, which must be analyzed in a short time frame in search of eventual fraud or suspicious transactions. In this work we have compiled a real data set from 2016 Brazilian elections for all city councils in the São Paulo state and used it to propose a framework of data segmentation analysis and validation. An exploratory data analysis is performed to determine the features distribution and to define the required feature pre-processing tasks. A clustering analysis using DBSCAN method is applied to a subset of the original data, focused on segmenting the spending data regarding contracts with car fuel providers and detecting potential outliers. Three clusters were identified and a ridge regression model was used to evaluate the most important features on cluster definition. One cluster was related to candidates that received zero votes and the remaining two discriminated suppliers if they had or not contracts almost exclusively related to candidate spending on car fuel. The hyperparameters from the clustering analysis were validated using a bootstrap method and a null hypothesis of data set structure randomness was rejected using a Monte Carlo approach.

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Fraud detection for job placement using hierarchical clusters-based deep neural networks

Cited by 34 publications

References 68 publications

Conical Classification For Efficient One-Class Topic Determination

Conical Classification For Efficient One-Class Topic Determination

Fraud Detection in Credit Cards using Logistic Regression

Spending Segmentation and Outlier Detection in Brazilian Elections

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