Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. ,  aim to improve prediction accuracy by applying Active Learning. To solve the imbalance issue, the active model was iteratively updated based on the G-mean, and the under sampling sampling was also applied. The proposed algorithm was suitable for large scale data since it did not need to use the whole data set to construct a model. The experiment was conducted on two standard corpuses, one of which contained more than 100,000 examples. The result shod that a prediction performance of standard technique (Neural Network) can be improved by applying the Active Learning strategy for 5%–13%. Furthermore, this technique also outperformed other classical classification algorithms including K-nearest neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB) and Artificial Neural Network (ANN).
['Phairod Tuntiwachiratrakun', 'Peerapon Vateekul']
I am writing a research paper on a idea Applying active learning strategy to classify large scale data with imbalanced classes
[{"from": "human", "value": "I am writing a research paper on a idea Applying active learning strategy to classify large scale data with imbalanced classes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. ,  aim to improve prediction accuracy by applying Active Learning. To solve the imbalance issue, the active model was iteratively updated based on the G-mean, and the under sampling sampling was also applied. The proposed algorithm was suitable for large scale data since it did not need to use the whole data set to construct a model. The experiment was conducted on two standard corpuses, one of which contained more than 100,000 examples. The result shod that a prediction performance of standard technique (Neural Network) can be improved by applying the Active Learning strategy for 5%\u201313%. Furthermore, this technique also outperformed other classical classification algorithms including K-nearest neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB) and Artificial Neural Network (ANN)."}]