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Book ChapterDOI

Decision Tree-Based Fraud Detection Mechanism by Analyzing Uncertain Data in Banking System

11 Feb 2020-pp 79-90
TL;DR: An improved novel choice tree for the two information is proposed, speaking to the development and a framework utilized for AI and information mining to strengthen the requirement of the financial undertaking is proposed.
Abstract: As of now, enormous electronic information stores are being kept up by banks and other money-related organizations. Information mining advancement gives the region to get to the right information at the right time from massive volumes of information. Data classification is an established issue in AI and information mining. In regular choice (decision) tree investigation, a normal for a tuple is either supreme or partial. The choice tree calculations are utilized for dissecting solid and numerical information of uses. In the surviving techniques, they play out the extended model of choice tree examination to help information tuple having factual characteristics with uncertainty characterized by discretionary pdf. Along these lines, we proposed an improved novel choice tree for the two information, speaking to the development and a framework utilized for AI and information mining to strengthen the requirement of the financial undertaking. This paper expects to evaluate the utilization of strategies for choice trees to aid the trepidation of bank extortion. The choice trees aid this work of choosing the characteristic that will build up a superior exhibition in determining the odds of bank fraud.
Citations
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Journal Article
TL;DR: Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results and be enhanced by the K-means algorithm.
Abstract: Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.

193 citations

Proceedings ArticleDOI
14 Dec 2022
TL;DR: In this article , the authors used physiological data gathered from a wearable device to simplify the procedure of psychological stress determination, which helps to distinguish the persons suffering from stress over healthy one.
Abstract: Mental stress is a normal and frequent phenomenon in human beings. Earlier stress recognition is critical for avoiding these negative impacts since prolonged stress has an adverse impact on mental health leading to anxiety, loss of sleep, or headache. Making utilize of physiological data gathered from a wearable device, present study attempts to simplify the procedure of psychological stress determination, which helps to distinguish the persons suffering from stress over healthy one. We tested our method using a dataset that was made accessible to the public. The precision of forecasting exact stress state was applied to make comparisons among effectiveness of numerous techniques of artificial intelligence (AI), including Artificial Neural Networks (ANN), Fusions of ANN with Support Vector Machines (SVM), Stack Classifying method, and Radial-basis Function (RF) Networks. The study included 3-class stress categorization method in which, results shown greatest accurate rating of 99.920percent by Stack Classifier, while RF provided the lowest preciseness of 84.462percent. Study outcome infer that the suggested models are efficient in detecting mental stress over time and show that physiological indicators could be highly relevant in identifying mental stress.

1 citations

Proceedings ArticleDOI
14 Dec 2022
TL;DR: In this article , the authors used physiological data gathered from a wearable device to simplify the procedure of psychological stress determination, which helps to distinguish the persons suffering from stress over healthy one.
Abstract: Mental stress is a normal and frequent phenomenon in human beings. Earlier stress recognition is critical for avoiding these negative impacts since prolonged stress has an adverse impact on mental health leading to anxiety, loss of sleep, or headache. Making utilize of physiological data gathered from a wearable device, present study attempts to simplify the procedure of psychological stress determination, which helps to distinguish the persons suffering from stress over healthy one. We tested our method using a dataset that was made accessible to the public. The precision of forecasting exact stress state was applied to make comparisons among effectiveness of numerous techniques of artificial intelligence (AI), including Artificial Neural Networks (ANN), Fusions of ANN with Support Vector Machines (SVM), Stack Classifying method, and Radial-basis Function (RF) Networks. The study included 3-class stress categorization method in which, results shown greatest accurate rating of 99.920percent by Stack Classifier, while RF provided the lowest preciseness of 84.462percent. Study outcome infer that the suggested models are efficient in detecting mental stress over time and show that physiological indicators could be highly relevant in identifying mental stress.
References
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Journal ArticleDOI
TL;DR: The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented and an algorithm for classification obtained by combining the basic rule discovery operations is given.
Abstract: The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented. Three classes of database mining problems involving classification, associations, and sequences are described. It is argued that these problems can be uniformly viewed as requiring discovery of rules embedded in massive amounts of data. A model and some basic operations for the process of rule discovery are described. It is shown how the database mining problems considered map to this model, and how they can be solved by using the basic operations proposed. An example is given of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm is efficient in discovering classification rules and has accuracy comparable to ID3, one of the best current classifiers. >

1,539 citations

Journal ArticleDOI
31 Aug 2004
TL;DR: It is shown that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods, and an optimization algorithm is described that can compute efficiently most queries.
Abstract: We describe a system that supports arbitrarily complex SQL queries on probabilistic databases. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is efficient query evaluation, a problem that has not received attention in the past. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.

1,113 citations

Journal ArticleDOI
TL;DR: The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection.
Abstract: We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.

738 citations

Journal ArticleDOI
01 Feb 1998
TL;DR: This work presents another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation, to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation.
Abstract: Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. We present another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation. The merger utilizes existing methodologies in both areas to full advantage, but is by no means trivial. In particular, knowledge inferences must be newly defined for the fuzzy tree. We propose a number of alternatives, based on rule-based systems and fuzzy control. We also explore capabilities that the new framework provides. The resulting learning method is most suitable for stationary problems, with both numerical and symbolic features, when the goal is both high knowledge comprehensibility and gradually changing output. We describe the methodology and provide simple illustrations.

666 citations

Journal ArticleDOI
TL;DR: A new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set.
Abstract: With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.

289 citations

Trending Questions (1)
How can AI-based fraud detection be improved in banking system?

The paper proposes an improved decision tree algorithm for analyzing uncertain data in the banking system to enhance fraud detection.