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|Title: ||filterNN: 基於神經網路之序列資料特徵選取方法|
filterNN: NN-based Feature Selection from Sequential Data
|Issue Date: ||2019-09-05 15:45:56 (UTC+8)|
We design a new Neural Network architecture which consists of two parts, filter, and classifier. We implement three kinds of filters, which can filter out unnecessary input data. The filtered data will be fed into the latter classifier to achieve the highest training accuracy. Because of the filter and classifier are trained together, thus, the filter will keep the inputs which help classifier to perform the classification. Therefore, the remaining input data can be viewed as the characteristic of the class. We also design three cost function to achieve different purpose, 1) the filtered inputs could be as less as possible, 2) the filtered inputs could be more consecutive as possible, 3) the classifier could achieve the highest training accuracy as possible. The three learning goals are in conflict with each other, so we demonstrate the tuning process in this research to achieve the best performance. We use text-based sequential data to test the usefulness of the proposed NN architecture. The use of text-based sequential data is malware execution API calls which are collected from the real world. The research shows that the proposed NN architecture is helpful for dealing with text-based sequential data and will filter the characteristic for domain experts to perform further analyze.
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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G1063560051|
|Data Type: ||thesis|
|Appears in Collections:||[資訊管理學系] 學位論文|
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