Estimation of Missing Values Using Optimised Hybrid Fuzzy C-Means and Majority Vote for Microarray Data

Abstract

Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.

Clustering column-mean quantile median: a new methodology for imputing missing data

“…When the missing ratio exceeds 20%, the prediction accuracy decreases; thus, choosing the appropriate imputation technique is challenging because the expectation precision decreases when the ratio is > 70% information loss [6]. Furthermore, MVs are identified by using the optimized hybrid of fuzzy C-means and majority vote (opt-FCMMV), where the majority vote (MV) and optimization are achieved by particle swarm optimization (PSO) on ovary and lung cancer datasets with 3 MV mechanisms (MCAR, MAR, and NMAR) and 5 different percentage values (5%, 10%, 30%, 50%, and 80%); the performance of this technique is better than that of FCM and FCMMV because it can improve accuracy, especially in case of high-dimensionality data [7]. Most studies have applied machine learning techniques to reduce the risks of missing genes, which is considering a single gene criterion as per the operation of the chosen technique.…”