Feature Selection via Robust Weighted Score for High Dimensional Binary
  Class-Imbalanced Gene Expression Data
Zardad Khan Amjad Ali Saeed Aldahmani
In this paper, a robust weighted score for unbalanced data (ROWSU) is
proposed for selecting the most discriminative feature for high dimensional
gene expression binary classification with class-imbalance problem. The method
addresses one of the most challenging problems of highly skewed class
distributions in gene expression datasets that adversely affect the performance
of classification algorithms. First, the training dataset is balanced by
synthetically generating data points from minority class observations. Second,
a minimum subset of genes is selected using a greedy search approach. Third, a
novel weighted robust score, where the weights are computed by support vectors,
is introduced to obtain a refined set of genes. The highest-scoring genes based
on this approach are combined with the minimum subset of genes selected by the
greedy search approach to form the final set of genes. The novel method ensures
the selection of the most discriminative genes, even in the presence of skewed
class distribution, thus improving the performance of the classifiers. The
performance of the proposed ROWSU method is evaluated on $6$ gene expression
datasets. Classification accuracy and sensitivity are used as performance
metrics to compare the proposed ROWSU algorithm with several other
state-of-the-art methods. Boxplots and stability plots are also constructed for
a better understanding of the results. The results show that the proposed
method outperforms the existing feature selection procedures based on
classification performance from k nearest neighbours (kNN) and random forest
(RF) classifiers.