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1
Content available Ensemble learning approach to enhancing binary classification in Intrusion Detection System for Internet of Thing
100%
Soni , Remli M. A. , Daud K. , Amien J. A.
International Journal of Electronics and Telecommunications
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2024
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tom Vol. 70, No. 2
465--472
EN
The Internet of Things (IoT) has experienced significant growth and plays a crucial role in daily activities. However, along with its development, IoT is very vulnerable to attacks and raises concerns for users. The Intrusion Detection System (IDS) operates efficiently to detect and identify suspicious activities within the network. The primary source of attacks originates from external sources, specifically from the internet attempting to transmit data to the host network. IDS can identify unknown attacks from network traffic and has become one of the most effective network security. Classification is used to distinguish between normal class and attacks in binary classification problem. As a result, there is a rise in the false positive rates and a decrease in the detection accuracy during the model's training. Based on the test results using the ensemble technique with the ensemble learning XGBoost and LightGBM algorithm, it can be concluded that both binary classification problems can be solved. The results using these ensemble learning algorithms on the ToN IoT Dataset, where binary classification has been performed by combining multiple devices into one, have demonstrated improved accuracy. Moreover, this ensemble approach ensures a more even distribution of accuracy across each device, surpassing the findings of previous research.
2
Content available Detection of source code in internet texts using automatically generated machine learning models
100%
Badurowicz M.
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2022
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tom Vol. 18, no 1
89--98
EN
In the paper, the authors are presenting the outcome of web scraping software allowing for the automated classification of source code. The software system was prepared for a discussion forum for software developers to find fragments of source code that were published without marking them as code snippets. The analyzer software is using a Machine Learning binary classification model for differentiating between a programming language source code and highly technical text about software. The analyzer model was prepared using the AutoML subsystem without human intervention and fine-tuning and its accuracy in a described problem exceeds 95%. The analyzer based on the automatically generated model has been deployed and after the first year of continuous operation, its False Positive Rate is less than 3%. The similar process may be introduced in document management in software development process, where automatic tagging and search for code or pseudo-code may be useful for archiving purposes.
3
Content available Failures prediction based on performance monitoring of a gas turbine: a binary classification approach
100%
Mulewicz B. , Marzec M. , Morkisz P. , Oprocha P.
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tom Vol. 26
9--21
EN
This paper is dedicated to employ novel technique of deep learning for machines failures prediction. General idea of how to transform sensor data into suitable data set for prediction is presented. Then, neural network architecture that is very successful in solving such problems is derived. Finally, we present a case study for real industrial data of a gas turbine, including results of the experiments.
4
Content available On the binary classification problem in discriminant analysis using linear programming methods
88%
OLUSOLA M. O. , ONYEAGU S. I.
Operations Research and Decisions
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2020
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tom 30
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nr 1
119-130
EN
This paper is centred on a binary classification problem in which it is desired to assign a new object with multivariate features to one of two distinct populations as based on historical sets of samples from two populations. A linear discriminant analysis framework has been proposed, called the minimised sum of deviations by proportion (MSDP) to model the binary classification problem. In the MSDP formulation, the sum of the proportion of exterior deviations is minimised subject to the group separation constraints, the normalisation constraint, the upper bound constraints on proportions of exterior deviations and the sign unrestriction vis-à-vis the non-negativity constraints. The two-phase method in linear programming is adopted as a solution technique to generate the discriminant function. The decision rule on group-membership prediction is constructed using the apparent error rate. The performance of the MSDP has been compared with some existing linear discriminant models using a previously published dataset on road casualties. The MSDP model was more promising and well suited for the imbalanced dataset on road casualties.
5
Content available On the binary classification problem in discriminant analysis using linear programming methods
88%
Olusola M. O. , Onyeagu S. I.
Operations Research and Decisions
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2020
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tom Vol. 30, No. 1
119--130
EN
This paper is centred on a binary classification problem in which it is desired to assign a new object with multivariate features to one of two distinct populations as based on historical sets of samples from two populations. A linear discriminant analysis framework has been proposed, called the minimised sum of deviations by proportion (MSDP) to model the binary classification problem. In the MSDP formulation, the sum of the proportion of exterior deviations is minimised subject to the group separation constraints, the normalisation constraint, the upper bound constraints on proportions of exterior deviations and the sign unrestriction vis-à-vis the non-negativity constraints. The two-phase method in linear programming is adopted as a solution technique to generate the discriminant function. The decision rule on group-membership prediction is constructed using the apparent error rate. The performance of the MSDP has been compared with some existing linear discriminant models using a previously published dataset on road casualties. The MSDP model was more promising and well suited for the imbalanced dataset on road casualties.
6
Content available Prediction of a Function of Misclassified Binary Data
75%
Al-Kandari N. M.
Statistics in Transition new series
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2016
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tom 17
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nr 3
429-448
EN
We consider the problem of predicting a function of misclassified binary variables. We make an interesting observation that the naive predictor, which ignores the misclassification errors, is unbiased even if the total misclassification error is high as long as the probabilities of false positives and false negatives are identical. Other than this case, the bias of the naive predictor depends on the misclassification distribution and the magnitude of the bias can be high in certain cases. We correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a biascorrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE).
7
Content available remote A comparison of deep convolutional neural networksfor image-based detection of concrete surface cracks
63%
Słoński M.
Computer Assisted Methods in Engineering and Science
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2019
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tom Vol. 26, no. 2
105--112
EN
The aim of this paper is to compare the performance of four deep convolutional neural networks in theproblem of image-based automated detection of concrete surface cracks in the case of a small dataset. Thiscrack detection problem is treated as a binary classification problem, and it is solved by training a deepconvolutional neural network on the small dataset. In this context, overfitting during training was the mainissue to cope with and various techniques were applied to overcome this issue. The results of the experi-ments suggest that the best approach for this problem is to use the pretrained convolutional base of a largepretrained convolutional neural network as an automatic feature extraction method and adding a new bi-nary classifier on top of the convolutional base. Then, at the training the new classifier and fine-tuningthe last few layers of the pretrained network take place at the same time. The classification accuracy of thebest deep convolutional neural network on the testing set is about 94%.
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