Sliding-window filtering

Lee, Chang-Hung; Lin, Cheng-Ru; Chen, Ming-Syan

doi:10.1145/502585.502630

Cited by 103 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Focus is on patterns that might result in fraud two clients who just so happen to share the same characteristics will undoubtedly act the same John et al, (2016). [51] Lee, C., et al [42], explain characteristics of sliding window filtering, utilises the ideas of cumulative filtering and scan reduction techniques to lower I/O and CPU costs while also efficiently regulating memory usage through the use of sliding-window partition. An ongoing time-variant transaction database can benefit greatly from effective incremental mining with the SWF algorithm.…”

Section: Ata Et Al (2020) [50] 10mentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

2023

IJAEML

View full text Add to dashboard Cite

Purpose: To understand the algorithms used in Credit Card Fraud Detection (CCFD) using Machine Learning (ML) and Data Mining (DM) techniques, Review key findings in the area and come up with research gaps or unresolved problem. To become knowledgeable about the current discussions in the area of ML and DM. Design/Methodology/Approach: The survey on CCFD using ML and DM was conducted based on data from academic papers, web articles, conference proceedings, journals and other sources. Information is reviewed and analysed. Results/Findings: Identification of credit card fraud is essential for protecting a person's or an organization's assets. Even though we have various safeguards in place to prevent fraudulent activity, con artists may develop a method to get around the checkpoints. We must create straightforward and efficient algorithms employing ML and DM to anticipate fraudulent activities in advance. Originality/Value: Study of ML and DM algorithms in CCFD from diverse sources is done. This area needs study due to recent methods by fraudsters in digital crime have developed. The information acquired will be helpful for creating new methodologies or improving the outcomes of current algorithms. Type of Paper: Literature Review.

show abstract

Section: Ata Et Al (2020) [50] 10mentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

2023

IJAEML

View full text Add to dashboard Cite

show abstract

“…Gharib, Nassar, Taha, and Abraham (2010) present an incremental algorithm based on the sliding‐window filtering algorithm (C.‐H. Lee, Lin, & Chen, 2001), which maintains temporal frequent itemsets after the temporal transaction database has been updated in order to reduce the time needed for generating new candidates. Fouad and Mostafa (2017) present a method for the efficient mining of incremental TARs, making use of a new data structure and of previously discovered frequent temporal itemsets.…”

Section: Considering Time As An Integral Component In the Mining Processmentioning

confidence: 99%

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segura‐Delgado

Gacto

Alcalá

et al. 2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed. This article is categorized under: Technologies > Association Rules Algorithmic Development > Association Rules

show abstract

“…The basic strategy of the traditional visual object detection algorithm is to first select the interested image region from the image, then extract the corresponding image features from the region, and then finally the extracted features are fed into the classifier. Such algorithms involve two aspects: feature extraction methods, such as the sliding window method [4], SIFT [5], HOG [6], etc., where the features are designed manually; and classifiers, such as SVM classifiers [7], Adaboost classifiers [8], etc. Because the traditional method needs to preselect the region, it will make the model time-consuming.…”

Section: Introductionmentioning

confidence: 99%

CF-YOLOX: An Autonomous Driving Detection Model for Multi-Scale Object Detection

Wang

2023

Sensors

View full text Add to dashboard Cite

In self-driving cars, object detection algorithms are becoming increasingly important, and the accurate and fast recognition of objects is critical to realize autonomous driving. The existing detection algorithms are not ideal for the detection of small objects. This paper proposes a YOLOX-based network model for multi-scale object detection tasks in complex scenes. This method adds a CBAM-G module to the backbone of the original network, which performs grouping operations on CBAM. It changes the height and width of the convolution kernel of the spatial attention module to 7 × 1 to improve the ability of the model to extract prominent features. We proposed an object-contextual feature fusion module, which can provide more semantic information and improve the perception of multi-scale objects. Finally, we considered the problem of fewer samples and less loss of small objects and introduced a scaling factor that could increase the loss of small objects to improve the detection ability of small objects. We validated the effectiveness of the proposed method on the KITTI dataset, and the mAP value was 2.46% higher than the original model. Experimental comparisons showed that our model achieved superior detection performance compared to other models.

show abstract

Sliding-window filtering

Cited by 103 publications

References 17 publications

Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

Temporal association rule mining: An overview considering the time variable as an integral or implied component

CF-YOLOX: An Autonomous Driving Detection Model for Multi-Scale Object Detection

Contact Info

Product

Resources

About