Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In classification tasks with imbalanced datasets the distri- bution of examples beten the classes is uneven. Hover, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping beten the classes is possibly the most significant one for a correct discrimination beten the classes. In this contribution  develop a novel proposal to deal with the for- mer problem developing a multi-objective evolutionary algorithm that optimizes both the number of variables and instances of the problem. Feature selection will allow to simplify the overlapping areas easing the generation of rules to distinguish beten the classes, whereas instance selection of samples from both classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as ll as removing noise and difficult borderline examples. Our experimental results, carried out using C4.5 decision tree as base- line classifier, show that this approach is very promising. Our proposal outperforms, with statistical differences, the results obtained with the SMOTE + ENN oversampling technique, which was shown to be a base- line methodology for classification with imbalanced datasets.
['Alberto Fernández', 'María José del Jesús', 'Francisco Herrera']
I am writing a research paper on a idea Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection
[{"from": "human", "value": "I am writing a research paper on a idea Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In classification tasks with imbalanced datasets the distri- bution of examples beten the classes is uneven. Hover, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping beten the classes is possibly the most significant one for a correct discrimination beten the classes. In this contribution  develop a novel proposal to deal with the for- mer problem developing a multi-objective evolutionary algorithm that optimizes both the number of variables and instances of the problem. Feature selection will allow to simplify the overlapping areas easing the generation of rules to distinguish beten the classes, whereas instance selection of samples from both classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as ll as removing noise and difficult borderline examples. Our experimental results, carried out using C4.5 decision tree as base- line classifier, show that this approach is very promising. Our proposal outperforms, with statistical differences, the results obtained with the SMOTE + ENN oversampling technique, which was shown to be a base- line methodology for classification with imbalanced datasets."}]