Srinilta, Chutimet
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Srinilta, Chutimet
Alternative Name
Srinilta, C.
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Email
chutimet.sr@kmitl.ac.th
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Item type:Publication, Application of Natural Neighbor-based Algorithm on Oversampling SMOTE Algorithms(2021-04-01); Kanharattanachai, SivakornClassification performance depends highly on data distribution. In real life, data often come imbalanced where one class is found more often than others. SMOTE-based algorithms are usually used to handle the class imbalance problem. One key parameter that algorithms in SMOTE family require is k-the number of nearest neighbors with respect to a certain data point. K that fits the dataset the most gives the optimum performance. This paper proposes an approach to suggest a value of the parameter k using Natural Neighbor algorithm. Datasets are made balanced by four SMOTE-based algorithms-standard SMOTE, Safe-Level-SMOTE, ModifiedSMOTE and Weighted-SMOTE. The F-measure and Recall matrices are used to evaluate classification performance of a Support Vector Machine classifier running against six datasets with different imbalance ratios. The results show that, the average classification performance achieved by the proposed k's is closer to the optimum when compared with the performance given by the default value of k. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Multi-task learning and thai handwritten text recognition(2020-07-01); Chatpoch, SuchakreeWritten languages have some commonalities and differences. Knowing one language can help learning other languages better. While labelled data is limited in offline Thai handwritten text recognition problem domain, multi-task learning is chosen to address the problem in our study. The multi-task model is trained from Thai, Latin and Devanagari writing scripts. The model consists of three layers employing Convolutional Neural Network, Recurrent Neural Network and Connectionist Temporal Classification, respectively. Recognition accuracies are compared against three corresponding single-task models. Thai handwritten text recognition performance is considerably improved by multi-task learning. Learning multiple languages helps the model generalize better when trained by large enough datasets.