Performance comparison of feature selection and extraction methods with random instance selection

Text classification is the automated technique used to classify text into a predefined category that is more related to text. Most studies and researches have focused on classification texts written in English rather than Arabic because of the Arabic nature and difficulty of its structures. The difficult nature of Arabic makes it more complex and difficult to deal with because of its many rules and characteristics that are unique to it, but it has become necessary to deal with this language because of its widespread use on the internet. Feature representation and extraction, especially for text, have attracted considerable attention in recent years. The main object of this process is to convert text into a numerical representation. All approaches and systems depend on the frequency of words within text, but these approaches are few. This paper presents a new feature extraction method aimed at furthering natural language processing applications in any language, especially Arabic. It is based on transforming a vector of representation in the time domain into the frequency domain. Fourier feature extraction is a powerful and versatile technique. The primary goal of this approach is to extract salient features from raw text, and then a filter is used to remove noise from the extracted features. Its scalability and efficiency make it suitable to be used on large datasets, making it a widely adopted feature extraction technique. For classification, we used logistic regression, which is a powerful tool for classifying text data and offers significant advantages such as speed and accuracy. We used three world datasets (CNN, OSAC, and SANAD), and the results showed a good performance. The proposed method outperformed recent approaches, in which accuracy reached 98%.

Aim of this paper is to apply Dimensionality Reduction techniques on a large clinical data of Type II diabetes patients in identifying the causes and symptoms that they tend to develop neuropathic complications. Data preprocessing is an essential technique using Machine Learning (ML) and Data Mining are ineffective for big data analytics. An effective approach to dimensionality reduction is reduction of the number of independent/dependent variables which are necessary for our analysis. These processes help in identifying the features to be considered for selection and extraction avoiding the redundant and irrelevant features by choosing subset features which are a linear combination of the original. Both supervised and unsupervised learning are applied for prediction and further analysis. This paper primarily focuses on Supervised Learning, the variables are known beforehand. Combination of feature selection techniques and ML algorithms are used to support practitioners with the best methods for feature reduction and extraction.

In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.

The paper proposes a hybrid approach to reduce dimension in text classification problems, to overcome the issue of Curse of Dimensionality. This hybrid approach is a combination of Feature Selection (FS) and Feature Extraction (FE) methods, considering different aspects of feature relevance, to effectively reduce the dimension in large text datasets. It prevents feature selection biased in favor of a particular FS method. Many FS methods like Term Variance, Document Frequency, Information Gain, Shannons Entropy measure, Mean-Median and Mean Absolute Difference, were implemented and a comparative study was made on their performance when implemented in a hybrid system. The features selected by the individual FS methods are merged using three approaches, namely, Union, Intersection and Modified Union. The sub lists of features further undergo Feature Extraction by PCA, and the reduced feature sub list is clustered with k-means. Finally, the sentiment-score of the individual clusters are calculated using SentiWordNet database which gives the polarity of the data. The experiments were conducted on the benchmark datasets namely Reuters-21,578 and Classic4. The performance evaluation of the system made using the measures like precision, recall, f-score and accuracy shows that the proposed method has improved performance compared to its competitive methods.

To recognize expressions accurately, facial expression systems require robust feature extraction and feature selection methods. In this paper, a normalized mutual information based feature selection technique is proposed for FER systems. The technique is derived from an existing method, that is, the max-relevance and min-redundancy (mRMR) method. We, however, propose to normalize the mutual information used in this method so that the domination of the relevance or of the redundancy can be eliminated. For feature extraction, curvelet transform is used. After the feature extraction and selection the feature space is reduced by employing linear discriminant analysis (LDA). Finally, hidden Markov model (HMM) is used to recognize the expressions. The proposed FER system (CNF-FER) is validated using four publicly available standard datasets. For each dataset, 10-fold cross validation scheme is utilized. CNF-FER outperformed the existing well-known statistical and state-of-the-art methods by achieving a weighted average recognition rate of 99 % across all the datasets.

Long range captured iris recognition system is a biometric system consisting of pattern recognition and computer vision. In the process of iris recognition, feature extraction and feature selection play a major role in increasing the recognition accuracy. This paper proposes feature extraction method using discrete cosine transform domain-based no-reference image quality assessment model, gray-level co-occurrence matrix, Hu seven moments, and statistical features. It also proposes fuzzy entropy and interval-valued fuzzy set measure-based feature selection method. The selected feature vectors are classified by neural network classifier. The model is tested with CASIA long range iris database. The recognition accuracy is compared with the results obtained without feature selection and existing feature selection methods. It has been observed that the fuzzy entropy method gives better classification accuracy than existing feature selection method. The results demonstrate that the proposed work is well suited to extract the features of iris polar images captured at a long distance and to reduce the dimensionality by selecting the useful features which increase the recognition accuracy .

Public sector and private organizations began using cybersecurity control in order to defend their assets against cybercriminals attack. Cybersecurity audits assist organizations to deal with cyber threats, cybercriminals, and cyber-attacks thatare growing in an aggressive cyber landscape. However, cyber-attacks and threats become more increase and complex in complicated cyber landscapes challenge auditors to perform an effective cybersecurity audit. This current situation puts in evidens ce the critical need for a new approach in the cybersecurity audit execution. This study reviews an alternative method in the execution of cybersecurity security checks. The analysis is on the character and behavioral of cyber-attacks and threats using feature extraction and selection method to get crucial elements from the common group of cyber-attacks and threats. Cyber-attacks and threats profile are systematic approaches driven by a clear understanding of the form of cyber-attacks and threats character and behavior patterns in cybersecurity requirements. As a result, this study proposes cyber-attacks and threats profiling for cybersecurity audit as a set of control elements that are harmonized with audit components that drive audits based on cyber threats.

Day by day the number of text documents in digital form is increasing. Text classification is used to organize these text documents. However, text classification has the problem of high dimensionality of feature space. This high dimensionality of feature space is solved by feature selection and feature extraction methods and improves the performance of text categorization. The feature selection and feature extraction techniques remove the irrelevant features from the text documents and reduce the dimensionality of feature space. This paper presents the various feature selection and feature extraction methods. This paper also discusses various classifiers to classify the documents.

In the field of fault diagnosis, machine learning is highly valued for its broad applicability and efficiency. Feature extraction and feature selection are key steps in the application of machine learning, and the performance of fault diagnosis methods relies heavily on the effective execution of these two steps. For this reason, this paper aims to enhance the performance of fault diagnosis methods by improving these two aspects. Firstly, to address the non-linearity and non-stationarity of rotating machinery vibration signals under variable operation conditions, this paper proposes an improved rapid refined composite multiscale sample entropy (IR2CMSE) feature extraction method. In addition, this paper decomposes the vibration signals with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and extracts the sensitive intrinsic modal functions’ (IMFs') IR2CMSE values (SI-IR2CMSE) as the initial feature vector, which more accurately reveals the intrinsic time-scale characteristics of the vibration signals. Secondly, to address the problem of over-reliance on sample labels in most feature selection methods, this paper proposes a semi-supervised Gaussian mixing model with sparse regularization (SSGMM-SR) feature selection model. The model does not require complete fault labels and can automatically identify important features. Finally, validation with two rotating machinery fault datasets shows that the method proposed in this study exhibits high diagnostic accuracy and stability across multiple classifiers.

Feature selection is very important step to improve the accuracy of speech emotion recognition for many applications such as speech-to-speech translation system. Thousands of features can be extracted from speech signal however which features are the most related for speaker emotional state. Until now most of related features to emotional states are not yet found. The purpose of this paper is to propose a feature selection method which have the ability to find most related features with linear or non-linear relationship with the emotional state. Most of the previous studies used either correlation between acoustic features and emotions as for feature selection or principal component analysis (PCA) as a feature reduction method. These traditional methods does not reflect all types of relations between acoustic features and emotional state. They only can find the features which have a linear relationship. However, the relationship between any two variables can be linear, nonlinear or fuzzy. Therefore, the feature selection method should consider these kind of relationship between acoustic features and emotional state. Therefore, a feature selection method based on fuzzy inference system (FIS) was proposed. The proposed method can find all features which have any kind of above mentioned relationships. Then A FIS was used to estimate emotion dimensions valence and activations. Third FIS was used to map the values of estimated valence and activation to emotional category. The experimental results reveal that the proposed features selection method outperforms the traditional methods.

A novel approach to improving C-Tree for feature selection

The goal of this work is to help the visually impaired by making it possible for them to recognize hidden images on signs, which will hopefully lead to fewer accidents. Although traffic signs are essential for maintaining order, they can also increase the risk of accidents if they are obstructed. Individuals who are visually impaired, such as the blind, can benefit greatly from this technique. Furthermore, the proposed system is conducive to the operation of autonomous cars, as the billboards can be read automatically and the vehicles may be directed by the proposed system. When a potentially dangerous image is received, a warning is displayed to the driver. Throughout this piece, ”input image” will refer to the footage recorded by a camera situated at the front of the car. After converting the image to grayscale, a resizing method is applied, and a filter is used to get rid of any noticeable noise that remains. In the end, feature extraction and feature reduction methods are used to priorities certain features over others. Hence, the DNN procedure is an integral aspect of the system, as it is used for both speech recognition and image recognition. This project’s main goal is to recognize incoming photos by comparing them to data sets stored in a database, with successful matches sounding an alarm for the driver.

The Support Vector Machine (SVM) (Cortes and Vapnik, 1995; Vapnik, 1995; Burges, 1998) is intended to generate an optimal separating hyperplane by minimizing the generalization error without the assumption of class probabilities such as Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called Support Vectors (SVs). In practice, these SVMs are a subset of the entire training data. By now, SVMs have been successfully applied in many applications, such as face detection, handwritten digit recognition, text classification, and data mining. Osuna et al. (1997) applied SVMs for face detection. Heisele et al. (2004) achieved high face detection rate by using 2nd degree SVM. They applied hierarchical classification and feature reduction methods to speed up face detection using SVMs. Feature extraction and reduction are two primary issues in feature selection that is essential in pattern classification. Whether it is for storage, searching, or classification, the way the data are represented can significantly influence performances. Feature extraction is a process of extracting more effective representation of objects from raw data to achieve high classification rates. For image data, many kinds of features have been used, such as raw pixel values, Principle Component Analysis (PCA), Independent Component Analysis (ICA), wavelet features, Gabor features, and gradient values. Feature reduction is a process of selecting a subset of features with preservation or improvement of classification rates. In general, it intends to speed up the classification process by keeping the most important class-relevant features.

This paper presents transient stability assessment of a large actual power system using the probabilistic neural network (PNN) with enhanced feature selection and extraction method. The investigated large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. An enhanced feature selection and extraction methods are then incorporated to reduce the input features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with enhanced feature selection and extraction methods reduces the time taken to train the PNN without affecting the accuracy of the classification results.

Increasing the number of car accidents and other cerebral disease cause to progress in using Brain-Compute Interface (BCI) as a common subject for research and treatment. The aim of Brain-Computer Interface system is to establish a new communication system that translates human intentions, reflected by brain signals, into a control signal for an output device such as a computer. To this end, different processes must be done on brain signals and these signals must be classified by suitable methods. There are various methods to classify ECoG signals which are different in features and classifiers. Used features depend on extracted features, feature reduction methods and measures of feature selection. So, for a specific data set, we can use different algorithms with different results. The purpose of this paper is finding the best algorithm to do a five-class finger flexion classification to choose flexed finger among one hand's fingers. To achieve this goal, after feature extraction, some different methods of feature reduction and classification examined on training data and the best algorithm is selected according to the achieved results.