4,054 research outputs found
Data mining for detecting Bitcoin Ponzi schemes
Soon after its introduction in 2009, Bitcoin has been adopted by
cyber-criminals, which rely on its pseudonymity to implement virtually
untraceable scams. One of the typical scams that operate on Bitcoin are the
so-called Ponzi schemes. These are fraudulent investments which repay users
with the funds invested by new users that join the scheme, and implode when it
is no longer possible to find new investments. Despite being illegal in many
countries, Ponzi schemes are now proliferating on Bitcoin, and they keep
alluring new victims, who are plundered of millions of dollars. We apply data
mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our
starting point is a dataset of features of real-world Ponzi schemes, that we
construct by analysing, on the Bitcoin blockchain, the transactions used to
perform the scams. We use this dataset to experiment with various machine
learning algorithms, and we assess their effectiveness through standard
validation protocols and performance metrics. The best of the classifiers we
have experimented can identify most of the Ponzi schemes in the dataset, with a
low number of false positives
Class-Imbalanced Learning on Graphs: A Survey
The rapid advancement in data-driven research has increased the demand for
effective graph data analysis. However, real-world data often exhibits class
imbalance, leading to poor performance of machine learning models. To overcome
this challenge, class-imbalanced learning on graphs (CILG) has emerged as a
promising solution that combines the strengths of graph representation learning
and class-imbalanced learning. In recent years, significant progress has been
made in CILG. Anticipating that such a trend will continue, this survey aims to
offer a comprehensive understanding of the current state-of-the-art in CILG and
provide insights for future research directions. Concerning the former, we
introduce the first taxonomy of existing work and its connection to existing
imbalanced learning literature. Concerning the latter, we critically analyze
recent work in CILG and discuss urgent lines of inquiry within the topic.
Moreover, we provide a continuously maintained reading list of papers and code
at https://github.com/yihongma/CILG-Papers.Comment: submitted to ACM Computing Survey (CSUR
a literature review
Fonseca, J., & Bacao, F. (2023). Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10, 1-37. [115]. https://doi.org/10.1186/s40537-023-00792-7 --- This research was supported by two research grants of the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”), references SFRH/BD/151473/2021 and DSAIPA/DS/0116/2019, and by project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generation for specific data formats and Machine Learning (ML) tasks. However, one of the most common data formats used in industrial applications, tabular data, is generally overlooked; Literature analyses are scarce, state-of-the-art methods are spread across domains or ML tasks and there is little to no distinction among the main types of mechanism underlying synthetic data generation algorithms. In this paper, we analyze tabular and latent space synthetic data generation algorithms. Specifically, we propose a unified taxonomy as an extension and generalization of previous taxonomies, review 70 generation algorithms across six ML problems, distinguish the main generation mechanisms identified into six categories, describe each type of generation mechanism, discuss metrics to evaluate the quality of synthetic data and provide recommendations for future research. We expect this study to assist researchers and practitioners identify relevant gaps in the literature and design better and more informed practices with synthetic data.publishersversionpublishe
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
VIGraph: Self-supervised Learning for Class-Imbalanced Node Classification
Class imbalance in graph data poses significant challenges for node
classification. Existing methods, represented by SMOTE-based approaches,
partially alleviate this issue but still exhibit limitations during imbalanced
scenario construction. Self-supervised learning (SSL) offers a promising
solution by synthesizing minority nodes from the data itself, yet its potential
remains unexplored. In this paper, we analyze the limitations of SMOTE-based
approaches and introduce VIGraph, a novel SSL model based on the
self-supervised Variational Graph Auto-Encoder (VGAE) that leverages
Variational Inference (VI) to generate minority nodes. Specifically, VIGraph
strictly adheres to the concept of imbalance when constructing imbalanced
graphs and utilizes the generative VGAE to generate minority nodes. Moreover,
VIGraph introduces a novel Siamese contrastive strategy at the decoding phase
to improve the overall quality of generated nodes. VIGraph can generate
high-quality nodes without reintegrating them into the original graph,
eliminating the "Generating, Reintegrating, and Retraining" process found in
SMOTE-based methods. Experiments on multiple real-world datasets demonstrate
that VIGraph achieves promising results for class-imbalanced node
classification tasks
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