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Investigating Machine Learning Approaches for Bitcoin Ransomware Payment Detection Systems

Kirat Jadhav
2020 International Journal of Innovative Science and Research Technology  
This paper attempts to examine the different machine learning approaches for efficiently identifying ransomware payments made to the operators using bitcoin transactions.  ...  The machine learning approaches are evaluated on bitcoin ransomware dataset.  ...  multi-class extremely imbalanced in nature.  ... 
doi:10.38124/ijisrt20sep784 fatcat:gg24sln3hbgz7fduaaooib4m2q

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Varun Dogra, Sahil Verma, Kavita Verma, Nz Jhanjhi, Uttam Ghosh, Dac-Nhuong Le
2022 International Journal of Interactive Multimedia and Artificial Intelligence  
The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques.  ...  The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms.  ...  data that can help efficient machine learning.  ... 
doi:10.9781/ijimai.2022.02.002 fatcat:rl7mwnqqwjf7bjfpfsxg2fsb2i

Learning from Imbalanced Data in Classification

2020 International journal of recent technology and engineering  
Continuous advancements of machine learning as well as mining data combining it with big data, a deep insight is required to understand the nature of learning imbalanced data.  ...  Imbalanced data learning is a research area and day by day development is going on.  ...  The challenges in learning imbalanced multi-instance as well as multi-label data are:  There is a requirement of skew-independent classifiers that classify multi-label data without using methods of resampling  ... 
doi:10.35940/ijrte.e6286.018520 fatcat:u62u7ylyjrh7xiww3u7agnljwa

Transfer learning for class imbalance problems with inadequate data

Samir Al-Stouhi, Chandan K. Reddy
2015 Knowledge and Information Systems  
We propose a novel boosting-based instance transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain  ...  Class imbalance classifiers are trained specifically for skewed distribution datasets.  ...  His primary research interests are in the area of machine learning with applications to localization via sensor fusion, transfer learning, multi-task learning and imbalanced learning.Chandan K.  ... 
doi:10.1007/s10115-015-0870-3 pmid:27378821 pmcid:PMC4929860 fatcat:qvlty4b4evfohd5fnsxxtk4y7y

Automated Approach To Classification Of Mine-Like Objects Using Multiple-Aspect Sonar Images

Xiaoguang Wang, Xuan Liu, Nathalie Japkowicz, Stan Matwin
2014 Journal of Artificial Intelligence and Soft Computing Research  
Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective  ...  The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers  ...  .: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data.  ... 
doi:10.1515/jaiscr-2015-0004 fatcat:lglqmlgpmnhxleu3hsaunoysgq

Learning from imbalanced data: open challenges and future directions

Bartosz Krawczyk
2016 Progress in Artificial Intelligence  
With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing  ...  Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research.  ...  Following challenges are to be faced in the field of learning from imbalanced multi-label and multi-instance data: -In multi-label learning there is a need for skew-insensitive classifiers that do not  ... 
doi:10.1007/s13748-016-0094-0 fatcat:ju77bqq7fbahjluiplybmtfdxq

Learning object from small and imbalanced dataset with Boost-BFKO

Liansheng Zhuang, Wei Zhou, Qi Tian, Nenghai Yu
2008 2008 IEEE International Conference on Multimedia and Expo  
In this paper, we introduce a novel learning algorithm Boost-BFKO, which combines boosting and data generation. It is suitable for small and imbalanced training datasets.  ...  One of the main drawbacks of boosting is its overfitting and poor predictive accuracy when the training dataset is small and imbalanced.  ...  Boost-BFKO is suitable to small datasets, as well as SVM, and can learn from imbalanced datasets.  ... 
doi:10.1109/icme.2008.4607718 dblp:conf/icmcs/ZhuangZTY08 fatcat:snk2f4jlqndsnh3niu6qh7ms6y

A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

Khan Md. Hasib, Md. Sadiq Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md. Imran Hossain Showrov, Shakil Ahmed, Obaidur Rahman
2020 Journal of Computer Science  
Over the last few years, several types of research have been carried out on the issue of class imbalance, including data sampling, cost-sensitive analysis, Genetic Programming based models, bagging, boosting  ...  learning techniques to fix problems with class imbalances.  ...  Acknowledgment We would like to thank Google and UCI Machine for providing the dataset and necessary information for this this research.  ... 
doi:10.3844/jcssp.2020.1546.1557 fatcat:ecgaztln6fecjnege3ne3yeqoe

Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data [article]

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li
2019 arXiv   pre-print
sufficient medical data provided for researchers to do training of machine learning models.  ...  However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting.  ...  In the other fold, we aimed at validating whether machine learning and deep learning can be used in EEG recordings multi-labels classification. Hence, we kept the original label for this part.  ... 
arXiv:1910.02544v1 fatcat:3go76afkvzatxfrafmy43wdn3m

Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li
2019 Machine Learning Research  
In conclusion, machine learning and deep learning demonstrated their potential usage in epileptic seizure identification using EEG raw data.  ...  In this study, we applied 6 machine learning algorithms (including naïve bayes, logistic regression, support vector machine, random forest and K-nearest neighbours and gradient boosting decision trees)  ...  No further imbalanced-against method is needed in the multi-label task. For the binary classification, some techniques were applied to adjust the weights of majority class.  ... 
doi:10.11648/j.mlr.20190403.11 fatcat:lujodxoturfs3dwpwpauiz4wgy

Imbalance class problems in data mining: a review

Haseeb Ali, Mohd Najib Mohd Salleh, Rohmat Saedudin, Kashif Hussain, Muhammad Faheem Mushtaq
2019 Indonesian Journal of Electrical Engineering and Computer Science  
We discuss the issues of classifiers which endorse bias for majority class and ignore the minority class.  ...  These problems impact the classification process negatively in machine learning process.  ...  ACKNOWLEDGEMENTS The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this research under Postgraduate Incentive Research Grant, Vote No.H334.  ... 
doi:10.11591/ijeecs.v14.i3.pp1552-1563 fatcat:ojcl3jbo6zc6hhtptpbsrvkgku

On the Class Imbalance Problem

Xinjian Guo, Yilong Yin, Cailing Dong, Gongping Yang, Guangtong Zhou
2008 2008 Fourth International Conference on Natural Computation  
In this case, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class since traditional classifiers seeking an accurate performance over a full range  ...  The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years.  ...  Conclusion Learning from imbalanced data sets is an important issue in machine learning.  ... 
doi:10.1109/icnc.2008.871 dblp:conf/icnc/GuoYDYZ08 fatcat:2uvodh4f5bbf5k5g5h6hl3cjlm

A Review of Clustering Technique Based on Different Optimization Function Using for Selection of Center Point

Kavita Firke
2017 International Journal for Research in Applied Science and Engineering Technology  
The process of clustering basically group the data based on feature attribute of data. the selection of features attribute of data based on the process of iteration.  ...  In this paper present the review of clustering technique for automatic validation and cluster center selection.  ...  At last, the weighted cost-sensitive ensemble classifier is constructed, and the dynamic cost-sensitive ensemble classification based on extreme learning machine classification is given.  ... 
doi:10.22214/ijraset.2017.3008 fatcat:3sjoz5ti3bg5voyxusyr5n22wu

A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang
2019 International Journal of Information and Communication Sciences  
In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning  ...  With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models.  ...  Zieba et al. (2016) proposed a novel approach for bankruptcy prediction that utilizes Extreme Gradient Boosting for learning an ensemble of decision trees [17] .  ... 
doi:10.11648/j.ijics.20190403.12 fatcat:e6c6svbh7nc6pdy2wctsjgfyc4

Under-sampling and feature selection algorithms for S2SMLP

Shudong Liu, Ke Zhang
2020 IEEE Access  
Imbalance learning is a hot topic in the data mining and machine learning domains.  ...  to select majority samples so as to construct a smaller training dataset for the classifier.  ...  For more information, see This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ... 
doi:10.1109/access.2020.3032520 fatcat:2dx3mpdsazhjdpx2xroob3x5oe
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