Learning from imbalanced data using methods of sample selection

Chairi, Ikram; Alaoui, Souad; Lyhyaoui, Abdelouahid

doi:10.1109/icmcs.2012.6320291

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…When a dataset is imbalanced, the approximation of the misclassification rate used in learning systems can contribute negatively to decrease the accuracy and the quality of learning [62].…”

Section: Mapping the Selected Data Sourcesmentioning

confidence: 99%

From Theory to Practice: A Data Quality Framework for Classification Tasks

2018

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Abstract:The data preprocessing is an essential step in knowledge discovery projects. The experts affirm that preprocessing tasks take between 50% to 70% of the total time of the knowledge discovery process. In this sense, several authors consider the data cleaning as one of the most cumbersome and critical tasks. Failure to provide high data quality in the preprocessing stage will significantly reduce the accuracy of any data analytic project. In this paper, we propose a framework to address the data quality issues in classification tasks DQF4CT. Our approach is composed of: (i) a conceptual framework to provide the user guidance on how to deal with data problems in classification tasks; and (ii) an ontology that represents the knowledge in data cleaning and suggests the proper data cleaning approaches. We presented two case studies through real datasets: physical activity monitoring (PAM) and occupancy detection of an office room (OD). With the aim of evaluating our proposal, the cleaned datasets by DQF4CT were used to train the same algorithms used in classification tasks by the authors of PAM and OD. Additionally, we evaluated DQF4CT through datasets of the Repository of Machine Learning Databases of the University of California, Irvine (UCI). In addition, 84% of the results achieved by the models of the datasets cleaned by DQF4CT are better than the models of the datasets authors.

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“…When a dataset is imbalanced, the approximation of the misclassification rate used in learning systems can contribute negatively to decrease the accuracy and the quality of learning [62].…”

Section: Mapping the Selected Data Sourcesmentioning

confidence: 99%

From Theory to Practice: A Data Quality Framework for Classification Tasks

2018

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Modeling Human-Machine Interaction by Means of a Sample Selection Method

Chairi

Griol

Molina

2015

Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection

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Unveiling the Potential of Random Undersampling in Geothermal Lithology Classification for Improved Geothermal Resource Exploration

Obika,

Okereke,

Eze

et al. 2024

SPE Nigeria Annual International Conference and Exhibition

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Lithology classification in geothermal exploration has been of great significance in the understanding of subsurface geology and geophysics, which can enhance the exploration and exploitation of geothermal resources. Alongside other known industrial means of classifying lithologies, the application of machine learning models has shown viable prospects in this regard. However, there seems to be poor accuracy in the performance of some of these models due to class imbalance associated with the lithologies to be classified. Hence, in this study, robust class imbalance handling techniques were investigated to efficiently classify lithology in a geothermal field. The investigated techniques which involved Synthetic Minority Oversampling Technique (SMOTE), Random Oversampling (RO), Random Undersampling (RU), and the Near Miss Undersampling (NMU) Techniques, were each employed with two ensemble bagging methods; Random Forest Classifier (RFC) and Balanced Bagging Classifier (BBC). F1 score was the key evaluation metric, as it considers both precision and recall, giving a more comprehensive picture of the models’ performance. It was observed that by leveraging real-time drilling data such as mud flow in, rate of penetration (ROP), surface torque, pump pressure and rotary speed as input parameters, RFC performed better with the resampling techniques than BBC did. Moreover, RFC combined with RU greatly outperformed other combination techniques in the prediction of the geothermal lithology with an F1 score of 93.6% for the minority class (Plutonic) and 99.3% for the majority class (Alluvium) on the testing dataset, while other combinations had F1 scores of less than 37%. This solution alongside other vital insights from this study, showed that class imbalance handling techniques can be efficiently adopted towards building more robust machine learning models for geothermal resource exploration with prevailing high temperature and unfavorable subsurface conditions that limit the use of known traditional methods.

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Learning from imbalanced data using methods of sample selection

Cited by 3 publications

References 3 publications

From Theory to Practice: A Data Quality Framework for Classification Tasks

From Theory to Practice: A Data Quality Framework for Classification Tasks

Modeling Human-Machine Interaction by Means of a Sample Selection Method

Unveiling the Potential of Random Undersampling in Geothermal Lithology Classification for Improved Geothermal Resource Exploration

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