OPFython: A Python implementation for Optimum-Path Forest

Rosa, Guilherme Jordão de Magalhães; Papa, João Paulo

doi:10.1016/j.simpa.2021.100113

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…Finally, the code employed in this work was implemented in Python 16 and based on the OPFython library [43,44].…”

Section: Methodsmentioning

confidence: 99%

Handling Imbalanced Datasets Through Optimum-Path Forest

Passos,

Jodas,

Ribeiro

et al. 2022

Preprint

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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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“…Finally, the code employed in this work was implemented in Python 16 and based on the OPFython library [43,44].…”

Section: Methodsmentioning

confidence: 99%

Handling Imbalanced Datasets Through Optimum-Path Forest

Passos,

Jodas,

Ribeiro

et al. 2022

Preprint

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“…In this study, several metrics are used to evaluate the performance of classifiers and figure out the optimal classifier for slope stability prediction [30,55]. The OPF k-NN and OPF are implemented based on the Python library "opfython" [53], and the k-NN, RBF-SVM, RF, DT, and LR are conducted on the Python library "scikitlearn" [54]. All experiments are conducted using a Windows1064 bits computer with 8Gb of RAM running an Intel ® Core™ i7-9700F CPU @ 3.00 GHz × 2.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Slope Stability Prediction Using k-NN-Based Optimum-Path Forest Approach

2023

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Slope instability can lead to catastrophic consequences. However, predicting slope stability effectively is still challenging because of the complex mechanisms and multiple influencing factors. In recent years, machine learning (ML) has received great attention in slope stability prediction due to its strong nonlinear prediction ability. In this study, an optimum-path forest algorithm based on k-nearest neighbor (OPFk-NN) was used to predict the stability of slopes. First, 404 historical slopes with failure risk were collected. Subsequently, the dataset was used to train and test the algorithm based on randomly divided training and test sets, respectively. The hyperparameter values were tuned by combining ten-fold cross-validation and grid search methods. Finally, the performance of the proposed approach was evaluated based on accuracy, F1-score, area under the curve (AUC), and computational burden. In addition, the prediction results were compared with the other six ML algorithms. The results showed that the OPFk-NN algorithm had a better performance, and the values of accuracy, F1-score, AUC, and computational burden were 0.901, 0.902, 0.901, and 0.957 s, respectively. Moreover, the failed slope cases can be accurately identified, which is highly critical in slope stability prediction. The slope angle had the most important influence on prediction results. Furthermore, the engineering application results showed that the overall predictive performance of the OPFk-NN model was consistent with the factor of safety value of engineering slopes. This study can provide valuable guidance for slope stability analysis and risk management.

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“…Figure 2 depicts such a pipeline. Implementation-wise, we rely on the supervised and unsupervised implementations provided by Opfython [31]. Additionally, the source code was implemented using Python and is available on GitHub.…”

Section: B Experimental Setupmentioning

confidence: 99%

$\text{O}^2$PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection

Passos,

Jodas,

Ribeiro

et al. 2021

Preprint

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Breast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, they still commonly present low accuracies and demand considerable time and effort from professionals. Therefore, a computer-aided diagnosis (CAD) system capable of providing early detection becomes hugely desirable. In the last decade, machine learningbased techniques have been of paramount importance in this context, since they are capable of extracting essential information from data and reasoning about it. However, such approaches still suffer from imbalanced data, specifically on medical issues, where the number of healthy people samples is, in general, considerably higher than the number of patients. Therefore this paper proposes the O 2 PF, a data oversampling method based on the unsupervised Optimum-Path Forest Algorithm. Experiments conducted over the full oversampling scenario state the robustness of the model, which is compared against three well-established oversampling methods considering three breast cancer and three general-purpose tasks for medical issues datasets.

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OPFython: A Python implementation for Optimum-Path Forest

Cited by 10 publications

References 14 publications

Handling Imbalanced Datasets Through Optimum-Path Forest

Handling Imbalanced Datasets Through Optimum-Path Forest

Slope Stability Prediction Using k-NN-Based Optimum-Path Forest Approach

$\text{O}^2$PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection

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