| dc.description.abstract | With the progress of Information Technology, people are benefited from efficient data collection and its related applications. In addition, since the number and the size of online databases grow rapidly, the way to retrieve useful information from these large databases effectively and efficiently is getting more important. This has become the research issue of data mining.
Data mining is a process of using a variety of statistical analyses or machine learing techniques for large amounts of data, including analyzing and managing the way of extracting the hidden values of features and their relevance to vairous applications. It helps people to learn novel knowledge by passing experiences that they can make the decision or forecaste the trend. However, from the retrieval process, there are some problems that should be considered, such as “Missing Values”.
Missing values can briefly defined as the (attribute) value that is missed in a chosen dataset. For example, when registering on websites, users have to fill in some columns sequentially, such as “Name”,”Birthday”…etc. However, because of some reasons, like data input errors, information concealing and so on, we may lost some data values through this process and these lost may cause data incomplete or some errors. Moreover, it can reduce the efficiency and accuracy of data mining results. In this case, people try to use some methods to impute missing values, and supervised learning algorithms is one of these common approach for the missing value impution problem.
In this thesis, I try to conduct experiments to compare the efficiency and accuracy between five famous supervised learning algorithms, which are Bayes, SVM, MLP, CART, k-NN, over categorical, numerical, and mix types of datasets. This allows us to know which imputation method performs better in what data type over the dataset with how many missing rates. The experimental results show that the CART method is the best choice for missing value imputation, which not only requires relative lower imputation time, but also can make the classifier provide the higher classification accuracy. | en_US |