| dc.description.abstract | In our current life, we not only face the huge data （Big Data） problem, but also need to take into account the immediacy of information. Under limited resources and
time, it is important to know how to perform data mining to find interesting style. We first consider data pre-processing for feature selection, and apply the selected data to construct the classifier, which could improve the classificaiton accuracy of the model, and help users make decisions.
In this thesis, we discuss the feature selection as the preprocessing step, and remove irrelevant and redundant features （ attributes of the data） from a given dataset. In other words, the feature selection algorithm is used to idenitfy useful or represenative attributes
from the entire data set. We reassemble these attributes into a new data set and then use the support vector machine classifier to improve the correctness and efficiency of the model.
Since most related studies only focus on single （competitive） feature selection, this thesis applies the concept of information fusion for multiple feature selection results. The experiments are based on 28 UCI public datasets. The purpose of this thesis is to
combine multiple feature selection methods. Under different dimensions and data types of information, we are able to understand whether combininng different feature selection results can perform better than single results in terms of classificaiton performance. | en_US |