Abstract
The aim of this research study is based on efficient gene selection and classification of microarray data analysis using hybrid machine learning algorithms. The beginning of microarray technology has enabled the researchers to quickly measure the position of thousands of genes expressed in an organic/biological tissue samples in a solitary experiment. One of the important applications of this microarray technology is to classify the tissue samples using their gene expression representation, identify numerous type of cancer. Cancer is a group of diseases in which a set of cells shows uncontrolled growth, instance that interrupts upon and destroys nearby tissues and spreading to other locations in the body via lymph or blood. Cancer has becomes a one of the major important disease in current scenario. DNA microarrays turn out to be an effectual tool utilized in molecular biology and cancer diagnosis. Microarrays can be measured to establish the relative quantity of mRNAs in two or additional organic/biological tissue samples for thousands/several thousands of genes at the same time. As the superiority of this technique become exactly analysis/identifying the suitable assessment of microarray data in various open issues. In the field of medical sciences multi-category cancer classification play a major important role to classify the cancer types according to the gene expression. The need of the cancer classification has been become indispensible, because the numbers of cancer victims are increasing steadily identified by recent years. To perform this proposed a combination of Integer-Coded Genetic Algorithm (ICGA) and Artificial Bee Colony algorithm (ABC), coupled with an Adaptive Extreme Learning Machine (AELM), is used for gene selection and cancer classification. ICGA is used with ABC based AELM classifier to chose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The performance of the proposed approach is evaluated and the results are compared with existing methods.
Highlights
Cancer detection and classification for diagnostic and prognostic purposes is usually based on pathological investigation of tissue section, resultant in individual interpretation of data (Eisen and Brown, 1999)
The limited information gained from morphological analysis/pathological investigation is often insufficient to aid in cancer diagnosis and may result in expensive but ineffective treatment of cancer
Saraswathi et al (2011) presented a novel approach which combines the optimization techniques such as Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO) for gene/feature selection and it can be coupled with the neural-network-based Extreme Learning Machine (ELM) for cancer classification
Summary
Cancer detection and classification for diagnostic and prognostic purposes is usually based on pathological investigation of tissue section, resultant in individual interpretation of data (Eisen and Brown, 1999). With the appearance and speedy development of DNA microarray technologies in previous work (Eisen and Brown, 1999; Lipshutz et al, 1999), classification of cancer by identification of corresponding gene expression profiles has previously concerned many efforts from a wide assortment of research communities. From this classification of cancer becomes major important to the diagnosis of diseases and treatment. Propose an Artificial Bee Colony algorithm (ABC) (Karaboga and Basturk, 2007) and driven Adaptive Extreme Learning Machine (AELM) (Jia and Hao, 2013), for managing the sparse/imbalanced data of classification problem that occurs in microarray data analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
