Solving the Graph Partitioning Based On Cuckoo Optimization Algorithm (COA)

Abstract: Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great success on dealing with missing values in data sets with heterogeneous attributes (their independent attributes are of different types) referred to as imputing mixed-attribute data sets. Epistatic miniarray profiling (E-MAP) is a powerful tool for analyzing gene functions and their biological relevance. However, E-MAP data suffers from large proportion of missing values, which often results in misleading and biased analysis results. This paper studies a new setting of missing data imputation, a novel ensemble approach EMDI based on the high-level diversity to impute missing values that consists of two global (kernel principal component analysis regression) and four local base estimators. The performance of the proposed KPCAR impute algorithm is compared with state-of-the-art linear regression methods, i.e., Bayesian principal component analysis imputation (BPCA). The KPCAR impute outperforms the L-Simpute when the missing percentage increases. The performance of the KPCA impute is similar to that of the BPCA imputation. Therefore, it is an effective and promising algorithm in estimating missing values for DNA microarray profiles.

Keywords: Epistatic miniarray profiling, missing value estimation, Matrix completion, KPCA.