Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy

Sáez, Juan Manuel; Galar, Mikel; Krawczyk, Bartosz

doi:10.1109/access.2019.2925300

Cited by 36 publications

(10 citation statements)

References 38 publications

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“…The problem can be addressed by either utilizing classifiers that can reduce the negative impact of this overlap or features can be dropped which reduce the overlapping regions. But dropping columns can also result in information loss 49 . To deal with this overlap of classes, we arrived at the conclusion to categorize 31 labels to 12.…”

Section: Methodsmentioning

confidence: 99%

Application layer classification of Internet traffic using ensemble learning models

2020

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Summary Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service‐level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade‐offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)‐based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic.

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Section: Methodsmentioning

confidence: 99%

Application layer classification of Internet traffic using ensemble learning models

2020

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“…OVO benefits the multi-class classification while increasing the separability of classes. Moreover, it was identified as an approach that highly benefits SVM as it provides robust results and superior performance [27]. Building and training of the SVM Models for linear, rbf, polynomial, sigmoid and precomputed kernels was done using the best combination of hyperparameters obtained through hyperparameter tuning.…”

Section: Lk(x Y)mentioning

confidence: 99%

Systematic Exploration and Classification of Useful Comments in Stack Overflow

Ranasinghe¹,

Chandimali²,

Wijesiriwardana³

2022

IJACSA

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Stack Overflow is a public platform for developers to share their knowledge on programming with an engaged community. Crowdsourced programming knowledge is not only generated through questions and answers but also through comments which are commonly known as developer discussions. Despite the availability of standard commenting guidelines on Stack Overflow, some users tend to post comments not adhering to those guidelines. This practice affects the quality of the developer discussion, thus adversely affecting the knowledgesharing process. Literature reveals that analyzing the comments could facilitate the process of learning and knowledge sharing. Therefore, this study intends to extract and classify useful comments into three categories: request clarification, constructive criticism, and relevant information. In this study, the classification of useful comments was performed using the Support Vector Machine (SVM) algorithm with five different kernels. Feature engineering was conducted to identify the possibility of concatenating ten external features with textual features. During the feature evaluation, it was identified that only TF-IDF and N-grams scores help classify useful comments. The evaluation results confirm Radial Basis Function (RBF) kernel of the SVM classification algorithm performs best in classifying useful comments in Stack Overflow regardless of the usage of the optimal combinations of hyperparameters.

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“…The kernel functions of the support vector machines used a radial basis function with a gamma parameter ranging from 0.1, 1, 10, 100 and 1000. For each value of gamma, the SVM was reinitialised 20times to increase the chance of obtaining an optimal classi er [71,73]. For Classi cation Statistics the algorithm of the SVM was trained for every target case in the dataset (each species) [71].…”

Section: Classi Cation Statistics With and Without Hybrids' Presencementioning

confidence: 99%

Counteracting Forces of Introgressive Hybridization and Interspecific Competition Shape the Morphological Traits of Cryptic Lberian Eptesicus Bats

Horta

Raposeira

Baños

et al. 2021

Preprint

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Cryptic species that coexist in sympatry are likely to simultaneously experience strong competition and hybridization. The first phenomenon would lead to character displacement, whereas the second can potentially promote morphological similarity through adaptive introgression. The main goal of this work was to investigate the effect of introgressive hybridization on the morphology of cryptic Iberian Eptesicus bats when facing counteracting evolutionary forces from interspecific competition. We found substantial overlap both in dentition and in wing morphology traits, though mainly in individuals in sympatry. The presence of hybrids contributes to a fifth of this overlap, with hybrids showing traits with intermediate morphometry. Thus, introgressive hybridization may contribute to species adaptation to trophic and ecological space responding directly to the macro-habitats characteristics of the sympatric zone and to local prey availability. On the other hand, fur shade tended to be browner and brighter in hybrids than parental species. Colour differences could result from partitioning of resources as an adaptation to environmental factors such as roost and microhabitats. We argue that a balance between adaptive introgression and niche partitioning shapes species interactions with the environment through affecting morphological traits under selection.

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Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy

Cited by 36 publications

References 38 publications

Application layer classification of Internet traffic using ensemble learning models

Application layer classification of Internet traffic using ensemble learning models

Systematic Exploration and Classification of Useful Comments in Stack Overflow

Counteracting Forces of Introgressive Hybridization and Interspecific Competition Shape the Morphological Traits of Cryptic Lberian Eptesicus Bats

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