| 摘要(英) |
In recent years, with the progress of the computing technology and the storage space, many researchers start to research the field of the Data Mining and the Big Data in order to find the value of numerous data and come up with innovative usages. Such as, but not limited to, using classifiers to discriminate the categories of articles and so on. When building a classifier, a more comprehensive training data will come to a better result., so that we select the training data in dataset and label the training data manually by experts. However, the cost of hiring experts is high and the output is limited, we have to select the comprehensive sample data to maximize the utility of training data. In other words, how to select the best training data in the unlabeled dataset with the constraint of the sample data number is the research purpose of this study
This study focused on using unsupervised learning to select samples with the constraint of the sample data number. In this thesis, we start to remove the outliers of the dataset, then we use K-Means to find the training data which contain all typical types in the datasets, after that, we use Balanced K-Means to cluster every clusters of K-means result according to the percentage of cluster size in the dataset. At last, we pick up the “centroid” as the best training data and label it by experts. These training materials then are modeled by five different classifiers to measure the classification of classifiers that were established by the select data. In other words, if the classification of classifiers that were established by the select data is good, it means the method we proposed can select the best training data under sample data number considerations.
Finally, the experimental results show that the method we proposed has good results in KNN、Naïve Bayes、SVM、MLP but Random Forest. According to this result, we can find that the classifier which is not established by the concept of space and the distance has the lower classification result, because it does not match the method designed concept of this study. On the other hand, the method we proposed can select the best training data with the constraint of sample data number when the classifier contains all of the attributes. |
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