Data Mining Practical Machine Learning Tools and Techniques

Computational Analysis of Microarray Data

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification

The emergence of DNA Microarray technology has enabled researchers to analyze the expression level of thousands of genes simultaneously. The Microarray data analysis is the process of finding the most informative genes as well as remove redundant and irrelevant genes. One of the most important applications of the Microarray data analysis is cancer classification. However, the curse of dimensionality and the curse of sparsity make classifying gene expression profiles a challenging task. One of the most effective methods to overcome these challenges is feature (gene) selection. In this paper, we aim to review and compare the most recent hybrid approaches that employ bio-inspired evolutionary methods as the wrapper method.

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A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification

Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naive Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.

A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

A novel approach for dimension reduction of microarray

Dimension reduction has become inevitable for pre-processing of high dimensional data. “Gene expression microarray data” is an instance of such high dimensional data. Gene expression microarray data displays the maximum number of genes (features) simultaneously at a molecular level with a very small number of samples. The copious numbers of genes are usually provided to a learning algorithm for producing a complete characterization of the classification task. However, most of the times the majority of the genes are irrelevant or redundant to the learning task. It will deteriorate the learning accuracy and training speed as well as lead to the problem of overfitting. Thus, dimension reduction of microarray data is a crucial preprocessing step for prediction and classification of disease. Various feature selection and feature extraction techniques have been proposed in the literature to identify the genes, that have direct impact on the various machine learning algorithms for classification and eliminate the remaining ones. This paper describes the taxonomy of dimension reduction methods with their characteristics, evaluation criteria, advantages and disadvantages. It also presents a review of numerous dimension reduction approaches for microarray data, mainly those methods that have been proposed over the past few years.

Dimension reduction methods for microarray data: a review

This paper proposed a new combination of feature selection/extraction approach for Artificial Neural Networks (ANNs) classification of high-dimensional microarray data, which uses an Independent Component Analysis (ICA) as an extraction technique and Artificial Bee Colony (ABC) as an optimisation technique. The study evaluates the performance of the proposed ICA + ABC algorithm by conducting extensive experiments on five-binary and one multi-class gene expression microarray data set and compared the proposed algorithm with ICA and ABC. The proposed method shows superior performance as it achieves the highest classification accuracy along with the lowest average number of selected genes. Furthermore, the present work compares the proposed ICA + ABC algorithm with popular filter techniques and with other similar bio-inspired algorithms with ICA. The experimental results show that the proposed algorithm gives more accurate classification rate for ANN classifier. Therefore, ICA + ABC are a promising approach for solving gene selection and cancer classification problems using microarray data.

Rabia Aziz

Papers

A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

A novel approach for dimension reduction of microarray

Dimension reduction methods for microarray data: a review

Artificial neural network classification of microarray data using new hybrid gene selection method

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