FEATURE SELECTION TECHNIQUES

In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and Naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was show...

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice.

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice.

In today's world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.

The DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification. These training data limitations constitute a challenge to certain classification methodologies. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the un wanted noisy and redundant genes This paper presents a review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification.

Microarray data classification is one of the most emerging clinical applications in the medical community. The classification process takes the detection of relevant and irrelevant probes into account, which is fundamental for subsequent classification. In this thesis, an efficient technique is proposed for the precise classification of microarray genes from the microarray gene expression dataset. The proposed classification technique performs the classification process with the aid of three phases namely, feature extraction, dimensionality reduction, and gene classification. Initially, Principal Component Analysis (PCA) is applied for dimension reduction and significant features are extracted from high dimension microarray data. The original data is projected to the lower dimension for selecting the eigenvector for the co-variance matrix shown cumulative variance up to a label of 100%. After the implementation of PCA, the reduced feature matrix so obtained is divided into two sets such as, training and test data set. Feed Forward Neural Network is a train using Back Propagation Algorithm and the performance accuracy of the classifier is tested using the test dataset (untrained samples). The total number of epochs required to reach the degree of accuracy is referred as the convergence rate. To check the efficiency of the algorithm the convergence rate is considered by plotting graphs between numbers of epochs vs. means square error (MSE). The two keys controlling parameters of BP algorithm are learning rate and momentum which affects the accuracy as well as the convergence rate. Varying the values of the parameters various graphs are plotted.

The microarray can track the expression levels of thousands of genes simultaneously. The high dimensional feature vectors of microarray impose a high dimensional cost as well as the risk of overfitting during classification. Thus it is necessary to reduce the dimension through ways like feature selection. Two basic approaches of feature selection appear: filter and wrapper techniques. In this paper, we make the people aware of the various techniques of feature selection. Keywords— Microarray, Feature Selection, Filter, Wrapper,

Classification of the different categories of small peptides is a challenging research area in bioinformatics research. However, machine learning based approaches are widely experimented in the literature with enormous success. For excellent learning of the classifiers, few numbers of informative features are important. This research explores a comparative study between various supervised feature selection methods such as Document Frequency (DF), Chi-Squared ( 2  ), Information Gain (IG), Gain Ratio (GR), Relief F (RF), and One R (OR). The corpus of small peptides data is selected from ARAPEP repository. Bayesian Classifier is taken to classify the different categories of the given corpus with the help of features selected by above feature selection techniques. Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and 2  were not so effective methods. Bayesian classifier has proven its worth in this study in terms of good performance accuracy and low false positives. Small peptides Identification, Machine Learning Classifiers, Pattern Recognition.

Classification of the different categories of small peptides is a challenging research area in bioinformatics research. However, machine learning based approaches are widely experimented in the literature with enormous success. For excellent learning of the classifiers, few numbers of informative features are important. This research explores a comparative study between various supervised feature selection methods such as Document Frequency (DF), Chi-Squared ( 2  ), Information Gain (IG), Gain Ratio (GR), Relief F (RF), and One R (OR). The corpus of small peptides data is selected from ARAPEP repository. Bayesian Classifier is taken to classify the different categories of the given corpus with the help of features selected by above feature selection techniques. Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and 2  were not so effective methods. Bayesian classifier has proven its worth in this study in terms of good performance accuracy and low false positives. Small peptides Identification, Machine Learning Classifiers, Pattern Recognition

Classification of the different categories of small peptides is a challenging research area in bioinformatics research. However, machine learning based approaches are widely experimented in the literature with enormous success. For excellent learning of the classifiers, few numbers of informative features are important. This research explores a comparative study between various supervised feature selection methods such as Document Frequency (DF), Chi-Squared ( 2  ), Information Gain (IG), Gain Ratio (GR), Relief F (RF), and One R (OR). The corpus of small peptides data is selected from ARAPEP repository. Bayesian Classifier is taken to classify the different categories of the given corpus with the help of features selected by above feature selection techniques. Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and 2  were not so effective methods. Bayesian classifier has proven its worth in this study in terms of good performance accuracy and low false positives. Small peptides Identification, Machine Learning Classifiers, Pattern Recognition

Classification of the different categories of small peptides is a challenging research area in bioinformatics research. However, machine learning based approaches are widely experimented in the literature with enormous success. For excellent learning of the classifiers, few numbers of informative features are important. This research explores a comparative study between various supervised feature selection methods such as Document Frequency (DF), Chi-Squared ( 2  ), Information Gain (IG), Gain Ratio (GR), Relief F (RF), and One R (OR). The corpus of small peptides data is selected from ARAPEP repository. Bayesian Classifier is taken to classify the different categories of the given corpus with the help of features selected by above feature selection techniques. Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and 2  were not so effective methods. Bayesian classifier has proven its worth in this study in terms of good performance accuracy and low false positives. Small peptides Identification, Machine Learning Classifiers, Pattern Recognition