Facilitating data preprocessing by a generic framework: a proposal for clustering

Kirchner, Kathrin; Zec, Jelena; Delibašić, Boris

doi:10.1007/s10462-015-9446-6

Cited by 23 publications

(10 citation statements)

References 55 publications

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“…Next, we provide a textual description of the complexity measures provided by DCol software 20 and used in our experiments as meta-features. A statistical description of these complexity measures can be found in [58].…”

Section: Appendix a Dmkb Metamodel Class Specificationmentioning

confidence: 99%

“…However, we would like to highlight some interesting approaches that could be incorporated in our framework for data-preprocessing (labeled as "external data pre-processing" in Figure 1). For example, the approach presented in [19] performs data pre-processing in an automatic manner with the support of meta-learning, while an approach for pipelining methods to facilitate further automated data pre-processing in presented in [20]. Also, there are domain-oriented data preprocessing approaches for characterizing automated data pre-processing such us the approach presented in [21] for environmental data.…”

Section: Introductionmentioning

confidence: 99%

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S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Espinosa

García‐Saiz

Zorrilla

et al. 2019

Computer Standards & Interfaces

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Data mining has proven to be very useful in order to extract information from data in many different contexts. However, due to the complexity of data mining techniques, it is required the know-how of an expert in this field to select and use them. Actually, adequately applying data mining is out of the reach of novice users which have expertise in their area of work, but lack skills to employ these techniques. In this paper, we use both model-driven engineering and scientific workflow standards and tools in order to develop named S3Mining framework, which supports novice users in the process of selecting the data mining classification algorithm that better fits with their data and goal. To this aim, this selection process uses the past experiences of expert data miners with the application of classification techniques over their own datasets. The contributions of our S3Mining framework are as follows: (i) an approach to create a knowledge base which stores the past experiences of experts users, (ii) a process that provides the expert users with utilities for the construction of classifiers' recommenders based on the existing knowledge base, (iii) a system that allows novice data miners to use these recommenders for discovering the classifiers that better fit for solving their problem at hand, and (iv) a public implementation of the framework's workflows. Finally, an experimental evaluation has been conducted to shown the feasibility of our framework.

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Section: Appendix a Dmkb Metamodel Class Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Espinosa

García‐Saiz

Zorrilla

et al. 2019

Computer Standards & Interfaces

View full text Add to dashboard Cite

show abstract

“…In the proposed criteria, in order to apply the effect of distance from fraudulent nodes, the PathElement component is used. Equations ( 5), (6), and (7) show how to calculate this factor.…”

Section: Distance From Fraudulence Node Factormentioning

confidence: 99%

“…For each path between i and F, ( 6) is calculated, and then according to (5), these amounts are calculated together. As mentioned in (7), according to the proposed method, PathElement of node i will be obtained by means of the calculated PathElement between i and each fraudulent node F. The more this value is, the more its effect is; and the less this value is, the less its effect is on Fake_score of the investigated…”

Section: Allfraudaccs Pathelement Imentioning

confidence: 99%

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Social Network Analysis for Fraud Detection

Baesens

Vlasselaer

Verbeke

2015

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

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Fraud detection is one of the ways to cope with damages associated with fraudulent activities that have become common due to the rapid development of the Internet and electronic business. There is a need to propose methods to detect fraud accurately fast. To achieve accuracy, fraud detection methods are required to consider both kinds of features, features based on the user level and features based on the network level. Therefore, in this paper, a method called MEFUASN is proposed to extract features based on social network analysis. After extracting these features, both the obtained features and the features based on user level are combined together to detect fraud using semi-supervised learning. Evaluation results show that using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy of detection remarkably, while it controls runtime in comparison with other methods.

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Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

Habib,

Khursheed

2024

Concurrency and Computation

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SummaryInternet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.

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Facilitating data preprocessing by a generic framework: a proposal for clustering

Cited by 23 publications

References 55 publications

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Social Network Analysis for Fraud Detection

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

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