Open AccessAn Effective Pre-Processing Phase for Gene Expression Classification
Author(s) -
Choon Sen Seah,
Shahreen Kasim,
Mohd Farhan Md Fudzee,
Mohd Saberi Mohamad,
Rd. Rohmat Saedudin,
Rohayanti Hassan,
Mohd Arfian Ismail,
Rodziah Atan
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v11.i3.pp1223-1227
Subject(s) - normalization (sociology) , bioconductor , computer science , random forest , raw data , expression (computer science) , data mining , data processing , artificial intelligence , value (mathematics) , machine learning , database , gene , biochemistry , chemistry , sociology , anthropology , programming language
A raw dataset prepared by researchers comes with a lot of information. Whether the information is usefull or not, completely depends on the requirement and purposes. In machine learning, data pre-processing is the very initial stage. It is a must to make sure the dataset is totally suitable for the requirement. In significant directed random walk (sDRW), there are three steps in data pre-processing stage. First, we remove unwanted attributes, missing value and proper arrangement, followed by normalization of the expression value and lastly, filtering method is applied. The first two steps are completed by Bioconductor package while the last step is works in sDRW.