Name
Title | ||
|---|---|---|
Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data |
Abstract | ||
|---|---|---|
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and x^2-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CSB.2004.35 | CSB |
Keywords | Field | DocType |
selecting informative genes,feature selection method,gain ratio,relief-f feature filtering algorithm,improved classification accuracy,microarray gene expression data,successful feature,relief-f algorithm,microarray data,cancer classification,cancer classification data set,previous classification problem,support vector machines,classification,cancer,learning artificial intelligence,feature selection,machine learning,leave one out cross validation,genetics,information gain | Data mining,Data set,Feature selection,Computer science,Artificial intelligence,Statistic,Pattern recognition,Support vector machine,Algorithm,Filter (signal processing),Bioinformatics,Information gain ratio,Linear classifier,Cross-validation,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2194-0 | 14 | 0.99 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhang Wang | 1 | 159 | 16.49 |
| Fillia Makedon | 2 | 1676 | 201.73 |