Dominant Feature Selection Research Articles

Fast dominant feature selection with compensation for efficient image steganalysis

Image steganalysis has witnessed significant development but still encounters challenges in detection speed and accuracy. Based on this consideration, this paper proposes a simple yet efficient dominant feature selection method. First, a separability measurement is designed utilizing the principles of “intraclass aggregation and interclass dispersion” and “maximum interclass disparity”, by which the contribution of each feature is better evaluated. Second, a separability measurement is presented considering the “holistic interclass disparity”, and thus dominant features are directly determined. Moreover, a compensation strategy is proposed to reduce the possibility of missing dominant features, thereby further enhancing the accuracy of the selected features. The effectiveness of the proposed method is empirically verified. Extensive experiments are conducted on the BOSSbase 1.01 and ALASKA2 datasets. The results show that, compared to some state-of-the-art works, the proposed method achieves better performance in terms of computational cost, feature dimension, and detection accuracy. In particular, the computational cost of the proposed method is extremely low.

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

Scale-selective and noise-robust extended local binary pattern for texture classification

As one of the most successful local feature descriptors, the local binary pattern (LBP) estimates the texture distribution rule of an image based on the signs of differences between neighboring pixels to obtain intensity- and rotation- invariance. In this paper, we propose a novel image descriptor to address scale transformation and noise interference simultaneously. We name it scale-selective and noise-robust extended LBP (SNELBP). First, each image in training sets is transformed into different scale spaces by a Gaussian filter. Second, noise-robust pattern histograms are obtained from each scale space by using our previously proposed median robust extended LBP (MRELBP). Then, scale-invariant histograms are determined by selecting the maximum among all scale levels for a certain image. Finally, the most informative patterns are selected from the dictionary pretrained by the two-stage compact dominant feature selection method (CDFS), maintaining the descriptor more lightweight with sufficiently low time cost. Extensive experiments on five public databases (Outex_TC_00011, TC_00012, KTH-TIPS, UMD and NEU) and one fresh texture database (JoJo) under two kinds of interferences (Gaussian and salt pepper) indicate that our SNELBP yields more competitive results than thirty classical LPB variants as well as eight typical deep learning methods.

Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.

Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine‐learning paradigm

AbstractLung cancer is one of the most deadly cancer in both men and women. Accurate and early diagnosis of pulmonary lung nodules is critical. This study presents an accurate computer‐aided diagnosis (CADx) system for risk stratification of pulmonary nodules in computed tomography (CT) lung images by fusing shape and texture‐based features in a machine‐learning (ML) based paradigm. A database with 114 (28 high‐risk) patients acquired from Lung Image Database Consortium (LIDC) is used in this study. After nodule segmentation using K‐means clustering, features based on shape and texture attributes are extracted. Seven different filter and wrapper‐based feature selection techniques are used for dominant feature selection. Lastly, the classification of nodules is performed by a support vector machine using six different kernel functions. The classification results are evaluated using 10‐fold cross‐validation and hold‐out data division protocols. The performance of the proposed system is evaluated using accuracy, sensitivity, specificity, and the area under receiver operating characteristics (AUC). Using 30 dominant features from the pool of shape and texture‐based features, the proposed system achieves the highest classification accuracy and AUC of 89% and 0.92, respectively. The proposed ML‐based system showed an improvement in risk stratification accuracy by fusing shape and texture‐based features.

Prediction of human phosphorylated proteins by extracting multi-perspective discriminative features from the evolutionary profile and physicochemical properties through LFDA

Protein phosphorylation is an emerging post-translational modification, which critically involved in the intracellular process of the human body by controlling diverse functions ranging from cell growth to metabolism. The existing experimental methods for identifying phosphorylated proteins are overpriced and resource-intensive; thus, it is necessary to develop a fast and accurate computational method to address the problem. Here we report a novel predictor HPhosPPred, a phosphorylated protein prediction method that is incorporating highly discriminative evolutionary and physicochemical information conserved in protein primary motifs, namely pseudo-position specific scoring matrix, the auto-covariance transformation of the position-specific scoring matrix and normalized moreau-broto auto-correlation. Further, to boost up the generalization capability of HPhosPPred, we used local fisher discriminant analysis as a dominant feature selection strategy for eliminating redundant and noise patterns from the extracted features. Finally, the optimized features feed to support vector machine with radial basis function kernel to predict phosphorylated proteins. As evident from the results, the proposed method achieved promising performance with an accuracy of 80.68%, sensitivity of 84.63%, specificity of 73.67%, and Matthew’s correlation coefficient of 0.581 using rigorous leave-one-out-cross-validation test and 10-fold cross-validation test. The empirical outcomes demonstrate that the developed model outperformed the existing state-of-the-art methods. Furthermore, our analysis reveals that the proposed tool can help detect unseen phosphorylated proteins in particular and proteomics research in general. The source code and dataset are publicly available at https://github.com/saeed344/HPhosPPred.

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound

Coronary artery disease risk assessment in intravascular ultrasound.A link between carotid and coronary grayscale plaque morphology.Principal component analysis (PCA) for dominant feature selection...

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology

Background and objectivePercutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. MethodThis system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). ResultsUsing the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. ConclusionsThis is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.

Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind

Psoriasis is an autoimmune skin disease with red and scaly plaques on skin and affecting about 125 million people worldwide. Currently, dermatologist use visual and haptic methods for diagnosis the disease severity. This does not help them in stratification and risk assessment of the lesion stage and grade. Further, current methods add complexity during monitoring and follow-up phase. The current diagnostic tools lead to subjectivity in decision making and are unreliable and laborious.This paper presents a first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing: (i) 11 higher order spectra (HOS) features, (ii) 60 texture features, and (iii) 86 color feature sets and their seven combinations. Aggregate 540 image samples (270 healthy and 270 diseased) from 30 psoriasis patients of Indian ethnic origin are used in our database. Machine learning using PCA is used for dominant feature selection which is then fed to support vector machine classifier (SVM) to obtain optimized performance. Three different protocols are implemented using three kinds of feature sets. Reliability index of the CADx is computed.Among all feature combinations, the CADx system shows optimal performance of 100% accuracy, 100% sensitivity and specificity, when all three sets of feature are combined. Further, our experimental result with increasing data size shows that all feature combinations yield high reliability index throughout the PCA-cutoffs except color feature set and combination of color and texture feature sets. HOS features are powerful in psoriasis disease classification and stratification. Even though, independently, all three set of features HOS, texture, and color perform competitively, but when combined, the machine learning system performs the best. The system is fully automated, reliable and accurate.

Genetic algorithms-based dominant feature selection for face detection application

A key challenge in computer vision applications is detecting objects in an image which is a non-trivial problem. One of the better performing proposed algorithms falls within the Viola and Jones framework. They make use of Adaboost for training a cascade of classifiers. The challenges of Adaboost-based face detector include the selection of the most relevant features which are considered as weak classifiers. However, selection of features based on lowering classification error leads to high computation complexity. To overcome this limitation, a novel genetic Adaboost is proposed in our work. In the same context of optimisation, a selection method based on Pareto concept of the most relevant features referred to as dominant features is proposed. This optimisation allows to reduce the initial feature space by 28%. Moreover, we notice that dominant features with genetic Adaboost further improve the performance of genetic Adaboost, reducing the total number of features by 20%.

Dynamic Genetic Algorithm-based Feature Selection Scheme for Machine Health Prognostics

This paper proposed a dominant feature selection scheme to enable the high performance prognostics of machine health. Statistical features were extracted from decomposed sub-modes by wavelet transform. Fisher ratio was employed to evaluate the extracted feature vectors, and dynamic searching strategy-based genetic algorithm was used to select the optimal feature subsets on the basis of maximizing the fitness function. Then dominant features with minimum mean square errors were used to predict the performance of machine health. Experimental results on predicting the lifetime of an unbalance vibration rotor system demonstrated that the proposed method can achieve better prognosis performance with less predicting errors.

Combined VMD-SVM based feature selection method for classification of power quality events

Power quality (PQ) issues have become more important than before due to increased use of sensitive electrical loads. In this paper, a new hybrid algorithm is presented for PQ disturbances detection in electrical power systems. The proposed method is constructed based on four main steps: simulation of PQ events, extraction of features, selection of dominant features, and classification of selected features. By using two powerful signal processing tools, i.e. variational mode decomposition (VMD) and S-transform (ST), some potential features are extracted from different PQ events. VMD as a new tool decomposes signals into different modes and ST also analyzes signals in both time and frequency domains. In order to avoid large dimension of feature vector and obtain a detection scheme with optimum structure, sequential forward selection (SFS) and sequential backward selection (SBS) as wrapper based methods and Gram–Schmidt orthogonalization (GSO) based feature selection method as filter based method are used for elimination of redundant features. In the next step, PQ events are discriminated by support vector machines (SVMs) as classifier core. Obtained results of the extensive tests prove the satisfactory performance of the proposed method in terms of speed and accuracy even in noisy conditions. Moreover, the start and end points of PQ events can be detected with high precision.

Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm

A large percentage of dermatologist׳s decision in psoriasis disease assessment is based on color. The current computer-aided diagnosis systems for psoriasis risk stratification and classification lack the vigor of color paradigm. The paper presents an automated psoriasis computer-aided diagnosis (pCAD) system for classification of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: (i) fulfills the color feature requirements and (ii) selects the powerful dominant color features while retaining high classification accuracy.Fourteen color spaces are discovered for psoriasis disease analysis leading to 86 color features. The pCAD system is implemented in a support vector-based machine learning framework where the offline image data set is used for computing machine learning offline color machine learning parameters. These are then used for transformation of the online color features to predict the class labels for healthy vs. diseased cases. The above paradigm uses principal component analysis for color feature selection of dominant features, keeping the original color feature unaltered. Using the cross-validation protocol, the above machine learning protocol is compared against the standalone grayscale features with 60 features and against the combined grayscale and color feature set of 146.Using a fixed data size of 540 images with equal number of healthy and diseased, 10 fold cross-validation protocol, and SVM of polynomial kernel of type two, pCAD system shows an accuracy of 99.94% with sensitivity and specificity of 99.93% and 99.96%. Using a varying data size protocol, the mean classification accuracies for color, grayscale, and combined scenarios are: 92.85%, 93.83% and 93.99%, respectively. The reliability of the system in these three scenarios are: 94.42%, 97.39% and 96.00%, respectively. We conclude that pCAD system using color space alone is compatible to grayscale space or combined color and grayscale spaces. We validated our pCAD system against facial color databases and the results are consistent in accuracy and reliability.

Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition

This paper develops a novel dominant feature selection method using a genetic algorithm with a dynamic searching strategy. It is applied in the search for the most representative features in rotary mechanical fault diagnosis, and is shown to improve the classification performance with fewer features. First, empirical mode decomposition (EMD) is employed to decompose a vibration signal into intrinsic mode functions (IMFs) which represent the signal characteristic with sample oscillatory modes. Then, a modified genetic algorithm with variable-range encoding and dynamic searching strategy is used to establish relationships between optimized feature subsets and the classification performance. Next, a statistical model that uses receiver operating characteristic (ROC) is developed to select dominant features. Finally, support vector machine (SVM) is used to classify different fault patterns. Two real-world problems, rotor-unbalance vibration and bearing corrosion, are employed to evaluate the proposed feature selection scheme and fault diagnosis system. Statistical results obtained by analyzing the two problems, and comparative studies with five well-known feature selection techniques, demonstrate that the method developed in this paper can achieve improvements in identification accuracy with lower feature dimensionality. In addition, the results indicate that the proposed method is a promising tool to select dominant features in rotary machinery fault diagnosis.

감성판별을 위한 생체신호기반 특징선택 분류기 설계

감성은 학습, 행동, 의사결정, 상호대화를 포함한 인간의 일상생활에 중요한 요소이다. 본 논문에서는 시스템의 복잡도를 줄이기 위하여 생체신호로부터 최소한의 중요한 특징만을 추출하여 사용하는 감성 분류기를 설계하고자 한다. 생체신호는 맥파,피부온도, 피부전도도, 뇌파신호(전두엽, 두정엽)를 사용하였으며, 4가지 감정(보통, 슬픔, 공포, 행복)은 영화 관람을 통하여 유도하였다. 측정한 생체신호로부터 추출한 24개의 특징으로부터 최적의 특징 집합의 결정은 서포트벡터머신 기반 적합도 함수를 사용하는 유전알고리즘을 적용하였다. 최적의 4감정 분류 정확도는 96.4%이었으며, 서포트벡터머신만을 사용하였을 경우보다 17% 높았다. 선택된 최소에러 특징은 맥파 심박변이도의 평균, NN50, 맥파 유도 맥파 전달 시간의 평균, 피부전도도의 평균과 두정엽 뇌파의 δ, β 주파수 대역에너지였다. 실험을 통하여 두정엽 뇌파, 맥파, 피부전도도의 조합이 고정밀 감정 장비에 적합하였으며, 79% 성능을 보인 맥파와 피부전도도의 조합이 간단한 감성장비에 적절하게 적용할 수 있다.

A histogram-based dominant wavelet domain feature selection algorithm for palm-print recognition

A feature extraction algorithm for palm-print recognition based on two dimensional discrete wavelet transform is proposed in this paper, which efficiently exploits the local spatial variations in a palm-print image. The palm-image is segmented into several spatial modules and a palm-print recognition scheme is developed, which extracts histogram-based dominant wavelet features from each of these local modules. The effect of modularization in terms of the entropy content of the palm-print images has been analyzed. The selection of dominant features for the purpose of recognition not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, the modularization of the palm-print image enhances the discriminating capabilities of the proposed features and thereby results in high within-class compactness and between-class separability of the extracted features. Different types of Daubechies wavelets (in terms of use of number of vanishing moments, i.e., db1–db10) have been utilized for the purpose of feature extraction and the effect upon the recognition performance has been also investigated. In order to further reduce the feature dimension, a principal component analysis is performed. It is found from our extensive experimentations on different palm-print databases that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.

Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification

Identification and prediction of a lifetime of industrial cutting tools using minimal sensors is crucial to reduce production costs and downtime in engineering systems. In this paper, we provide a formal decision software tool to extract the dominant features enabling tool wear prediction. This decision tool is based on a formal mathematical approach that selects dominant features using the singular value decomposition of real-time measurements from the sensors of an industrial cutting tool. Selection of dominant features is important, as retaining only essential features allows reduced signal processing or even reduction in the number of required sensors, which cuts costs. It is shown that the proposed method of dominant feature selection is optimal in the sense that it minimizes the least-squares estimation error. The identified dominant features are used with the recursive least squares (RLS) algorithm to identify parameters in forecasting the time series of cutting tool wear. Experimental results on an industrial high-speed milling machine show the effectiveness in predicting the tool wear using only the dominant features.