Improving optimum-path forest learning using bag-of-classifiers and confidence measures

Fernandes, Silas Evandro Nachif; Papa, João Paulo

doi:10.1007/s10044-017-0677-9

Cited by 12 publications

(3 citation statements)

References 31 publications

(39 reference statements)

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“…Assigning scores is straightforward using the OPF algorithm, as demonstrated by Fernandes and Papa [35]. During validation, we computed how many times a training sample labeled a validation node correctly.…”

Section: Undersampling With Optimum-path Forestmentioning

confidence: 99%

Handling Imbalanced Datasets Through Optimum-Path Forest

Passos,

Jodas,

Ribeiro

et al. 2022

Preprint

Self Cite

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In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Such an advance is partially due to the exponential growth in the amount of data available, which makes it possible to extract trustworthy real-world information from them. However, such data is generally imbalanced since some phenomena are more likely than others. Such a behavior yields considerable influence on the machine learning model's performance since it becomes biased on the more frequent data it receives. Despite the considerable amount of machine learning methods, a graph-based approach has attracted considerable notoriety due to the outstanding performance over many applications, i.e., the Optimum-Path Forest (OPF). In this paper, we propose three OPF-based strategies to deal with the imbalance problem: the O 2 PF and the OPF-US, which are novel approaches for oversampling and undersampling, respectively, as well as a hybrid strategy combining both approaches. The paper also introduces a set of variants concerning the strategies mentioned above.Results compared against several state-of-the-art techniques over public and private datasets confirm the robustness of the proposed approaches.

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Section: Undersampling With Optimum-path Forestmentioning

confidence: 99%

Handling Imbalanced Datasets Through Optimum-Path Forest

Passos,

Jodas,

Ribeiro

et al. 2022

Preprint

Self Cite

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“…The OPF presented results similar to SVM and better than neural networks and Bayesian classifiers. The biggest difference is the execution time, which can be faster depending on the size of the database [52] [53].…”

Section: Opf Classifiersmentioning

confidence: 99%

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

Macedo¹,

Maia²,

Rabbani³

et al. 2020

JGIS

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One of the principal difficulties related to road safety management in Brazil is the lack of data on road projects, especially those on rural roads, which makes it difficult to use road safety studies and models from other countries as a reference. Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. However, accurately recognizing and extracting hyperspectral images from roads has been recognized as a challenging task in the processing of hyperspectral data. In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared. Both classifiers produced reasonable results, however, the OPF algorithm outperformed SVM. The higher classification accuracy obtained by the OPF was mainly attributed to the ability to better distinguish between regions of exposed soil and unpaved roads.

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“…After the completion of 10 experiments, the average of 10 experiments of each indicator was used to evaluate the performance of the model. This paper uses four indicators to evaluate the performance of the model, namely accuracy, precision, recall, and F-measure values (harmonic average of precision and recall) [43]. The recall rate indicates the ratio of the number of unsafe connections detected to the actual number of connections.…”

Section: A Experimental Data Setmentioning

confidence: 99%

Network Security Situation Prediction Based on MR-SVM

Zhang

Liu

et al. 2019

IEEE Access

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The support vector machine (SVM) is verified to be effective for predicting cyber security situations, however, the long training time of the prediction model is a drawback to its use. To address this, a cyber security situation prediction model based on MapReduce and the SVM is proposed. The base classifier for this model uses an SVM, and parameter optimization is performed by the Cuckoo Search (CS) to determine the optimal parameters of the SVM. Considering the problem of time cost when a data set is large, we choose to use MapReduce to perform distributed training on SVMs to improve training speed. ''Map'' is used to map distributed training network security situation data, and ''Reduce'' merges and sorts the prediction results. Experimental results show that the proposed prediction model has improved the accuracy and decreased the training time cost compared to the traditional model.

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Improving optimum-path forest learning using bag-of-classifiers and confidence measures

Cited by 12 publications

References 31 publications

Handling Imbalanced Datasets Through Optimum-Path Forest

Handling Imbalanced Datasets Through Optimum-Path Forest

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

Network Security Situation Prediction Based on MR-SVM

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