Abstract
One of the most popular data mining techniques is classification which assigns collection items to classes. The main goal of classification is to accurately predict the target class for each data cases. One of the popular application areas of machine learning is IMAGE SEGMENTATION. The trained classifiers are used to extract relevant features of the target region [1]. In this paper, experiments are conducted using WEKA (Waikato Environment for Knowledge Analysis) tool. It is an open source. It contains the collection of machine learning algorithms for data mining purpose. We performed preprocessing with or without resample filter on the imbalanced image segmentation dataset and experiments are conducted with the most popular classifiers namely J48(C4.5), Naive Bayes, Random Forest and SMO on this resultant dataset [2]. The resample filter will adds feature subset of data samples to the imbalanced dataset. Finally, our methodology improves the performance of the classifiers with the resample filter.
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Naga RamaDevi, G., Janga Reddy, M., Baswaraj, D. (2020). Improve the Efficiency of the Classifiers Using Resample Technique on Image Segmentation Dataset. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_102
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DOI: https://doi.org/10.1007/978-981-15-1420-3_102
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