An
Additive Sparse Logistic Regularization Method for Cancer Classification in
Microarray Data
Vijay
Suresh Gollamandala1 and Lavanya Kampa1,2
1Department of Computer Science and Engineering,
Lakireddy Bali Reddy College of Engineering, India
2Department of Information Technology,
Lakireddy Bali Reddy College of Engineering, India
Abstract: Now
a day’s cancer has become a deathly disease due to the abnormal growth of the
cell. Many researchers are working in this area for the early prediction of
cancer. For the proper classification of cancer data, demands for the
identification of proper set of genes by analyzing the genomic data. Most of
the researchers used microarrays to identify the cancerous genomes. However,
such kind of data is high dimensional where number of genes are more compared
to samples. Also the data consists of many irrelevant features and noisy data.
The classification technique deal with such kind of data influences the
performance of algorithm. A popular classification algorithm (i.e., Logistic
Regression) is considered in this work for gene classification. Regularization
techniques like Lasso with L1
penalty, Ridge with L2 penalty, and hybrid Lasso
with L1/2+2 penalty used to minimize irrelevant
features and avoid overfitting. However, these methods are of sparse parametric
and limits to linear data. Also methods have not produced promising performance
when applied to high dimensional genome data. For solving these problems, this
paper presents an Additive Sparse Logistic Regression with Additive
Regularization (ASLR) method to discriminate linear and non-linear variables in
gene classification. The results depicted that the proposed method proved to be
the best-regularized method for classifying microarray data compared to
standard methods.
Keywords: Microarray data, sparse regularization, feature selection, logistic regression, and lasso.
Received April 30, 2020; accepted September 17, 2020
https://doi.org/10.34028/iajit/18/2/10