SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fuzzy Cluster based Nearest Neighbor Classifier

In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis

Background and Objectives: This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. Methods: In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. Results: It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Sup- port Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). Conclusions: It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society

Selección de Características de Microarreglos de ADN Utilizando una Búsqueda Cuckoo

En este artículo, se propone un método híbrido para la selección y clasificación de datos de microarreglos de AND. Primero, el método combina los subconjuntos de genes relevantes obtenidos de cinco métodos de filtro, después, se implementa un algoritmo basado en una búsqueda cuckoo combinado con un clasificador MSV. El algoritmo híbrido explora dentro del subconjunto obtenido en la etapa anterior y selecciona los genes que alcanzan un alto desempeño al entrenar al clasificador. En los resultados experimentales, el algoritmo obtiene una tasa de clasificación alta seleccionado un número pequeño de genes, los resultados obtenidos son comparados con otros métodos reportados en la literatura

Pareto optimal-based feature selection framework for biomarker identification

Numerous computational techniques have been applied to identify the vital features of gene expression datasets in aiming to increase the efficiency of biomedical applications. The classification of microarray data samples is an important task to correctly recognise diseases by identifying small but clinically meaningful genes. However, identification of disease representative genes or biomarkers in high dimensional microarray gene-expression datasets remains a challenging task. This thesis investigates the viability of Pareto optimisation in identifying relevant subsets of biomarkers in high-dimensional microarray datasets. A robust Pareto Optimal based feature selection framework for biomarker discovery is then proposed. First, a two-stage feature selection approach using ensemble filter methods and Pareto Optimality is proposed. The integration of the multi-objective approach employing Pareto Optimality starts with well-known filter methods applied to various microarray gene-expression datasets. Although filter methods provide ranked lists of features, they do not give information about optimum subsets of features, which are namely genes in this study. To address this limitation, the Pareto Optimality is incorporated along with filter methods. The robustness of the proposed framework is successfully demonstrated on several well-known microarray gene expression datasets and it is shown to achieve comparable or up to 100% predictive accuracy with comparatively fewer features. Better performance results are obtained in comparison with other approaches, which are single-objective approaches. Furthermore, cross-validation and k-fold approaches are integrated into the framework, which can enhance the over-fitting problem and the gene selection process is subsequently more accurate under various conditions. Then the proposed framework is developed in several phases. The Sequential Forward Selection method (SFS) is first used to represent wrapper techniques, and the developed Pareto Optimality based framework is applied multiple times and tested on different data types. Given the nature of most real-life data, imbalanced classes are examined using the proposed framework. The classifier achieves high performance at a similar level of different cases using the proposed Pareto Optimal based feature selection framework, which has a novel structure for imbalanced classes. Comparable or better gene subset sizes are obtained using the proposed framework. Finally, handling missing data within the proposed framework is investigated and it is demonstrated that different data imputation methods can also help in the effective integration of various feature selection methods