Binary Grey Wolf Optimizer based Feature Selection for Nucleolar and Centromere Staining Pattern Classification in Indirect Immunofluorescence Images
01 Jul 2019-Vol. 2019, pp 7040-7043
TL;DR: Results indicate that the prominent features obtained using BGWO can improve the discrimination performance of IIF staining patterns and appear to enable the computer aided diagnosis of autoimmune diseases.
Abstract: In this work, an attempt is made to distinguish nucleolar and centromere staining patterns using Bag-of-Keypoint Features (BoKF) model and Binary Grey Wolf Optimization (BGWO) based feature selection. Fluorescent staining patterns are produced by Indirect Immunofluorescence (IIF) Imaging and the patterns considered for this study are taken from a publicly available online database. The IIF images are pre-processed using edge-aware local contrast enhancement method. The contrast enhanced images are subjected to BoKF framework and Speeded up Robust Feature keypoints are extracted. Further, the most significant features are identified using BGWO and are fed to k-Nearest Neighbor (kNN) for classification. The results show that the BGWO features are able to classify the nucleolar and centromere patterns with an average accuracy of 91.6%. Results also indicate that the prominent features obtained using BGWO can improve the discrimination performance of IIF staining patterns. Hence it appears that the BGWO based feature selection could enable the computer aided diagnosis of autoimmune diseases.
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TL;DR: In this article, an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019) is presented, and a categorical list of more than a hundred metaheuristics algorithms is presented.
Abstract: Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.
182 citations
TL;DR: A Modified Binary GWO based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance and shows the superiority of the proposed algorithm compared to binary versions of the-state-of-the-art optimization techniques.
Abstract: Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.
97 citations
Cites methods from "Binary Grey Wolf Optimizer based Fe..."
...In [34], a method based on Bag-of-Keypoint Features (BoKF) model...
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TL;DR: A novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm and a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems.
Abstract: Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.
17 citations
Cites methods from "Binary Grey Wolf Optimizer based Fe..."
...[36] exploit a binary GWO (BGWO)based feature selection method and the bag-of-keypoint features (BoKF) model to distinguish nucleolar and centromere staining patterns....
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30 Sep 2021
TL;DR: Experimental results show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.
Abstract: Grey Wolf Optimizer (GWO) is a nature-inspired swarm intelligence algorithm that mimics the hunting behavior of grey wolves. GWO, in its basic form, is a real coded algorithm that needs modifications to deal with binary optimization problems. In this paper, previous work on the binarization of GWO are reviewed, and are classified with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named SetGWO), which is based on set encoding and uses set operations in its updating strategy. The proposed algorithm uses a completely different encoding scheme that eliminates the need for the transfer function and boundary checking, and also uses lower-dimensional agents; therefore, decreases the running time. Also, by using an exclusive exploration set for each agent, defining a different distance measure and an encircling strategy in discrete spaces, the quality of solutions has been improved. Experimental results on different real-world combinatorial optimization problems and datasets show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.
3 citations
01 Dec 2019
TL;DR: The paper uses the Analysis of Variance (ANOVA) for the identification of appropriate features and Artifical Neural network (ANN) for classification of ANA HEp2 cells pattern and indicates that textural features are the better features in comparison with other extracted features.
Abstract: Indirect Immunfluorsece method (IFA) is one of the important laboratory procedures for the diagnosis of the autoimmune disease, but it suffers from low throughput and subjectivity due to manual interpretation. The Human Epithelial type-2 (HEp-2) pattern, such as homogeneous, speckled, centromere, Nucleolar pattern images, gives the diagnosis of different autoimmune diseases. For the current study, different patterns are obtained from the publicly available datasets A.I.D.A ((Auto- Immunity Diagnosis by Computer) project of 1000 images. The images pre-processed and features such as statistical and textural features extracted and explored to find the appropriate one for the detection and the classification of ANA HEp2 cells pattern. The paper uses the Analysis of Variance (ANOVA) for the identification of appropriate features and Artifical Neural network (ANN) for classification. The result obtained indicates that textural features are the better features in comparison with other extracted features, with the results obtained average accuracy around 92% using ANN as the classifier. The outcome thus produced is useful for the further design of cost-effective image analysis in the autoimmune diagnosis
1 citations
Cites methods from "Binary Grey Wolf Optimizer based Fe..."
...The nucleolar and centromere patterns differentiated in work [14-18] using Bag-of- Key point features (BoKF) and Binary Grey Wolf Optimization (BGWO) based feature selection method and classification accuracy reported is 91 %....
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References
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TL;DR: Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.
958 citations
TL;DR: An adaptive image equalization algorithm that automatically enhances the contrast in an input image that is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.
Abstract: In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.
213 citations
TL;DR: A variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm.
Abstract: In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent’s own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents’ states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.
139 citations
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TL;DR: An attempt has been made to analyse the shape changes of Corpus Callosum (CC) using shape based Laplace Beltrami (LB) eigen value features and machine learning techniques, which seems to be clinically significant in the shape investigation of brain structures for AD diagnosis.
Abstract: Reaction diffusion level set is used to segment Corpus Callosum (CC) in MR images.Shape changes of CC are analysed using Laplace Beltrami (LB) eigen values.Classifiers are used to evaluate the discriminative power of LB eigen values.Distinct differentiation is obtained for normal and Alzheimer conditions.KNN could provide maximum accuracy of 93.37% in the classification of AD subjects. Automated study of brain sub-anatomic region like Corpus Callosum (CC) is challenging due to its complex topology and varying shape. The development of reliable Computer Aided Diagnosis (CAD) systems would help in the early detection of Alzheimer's Disease (AD) and to perform drug trails to palliate the effect of AD. In this work, an attempt has been made to analyse the shape changes of CC using shape based Laplace Beltrami (LB) eigen value features and machine learning techniques. CC from the normal and AD T1-weighted magnetic resonance images are segmented using Reaction Diffusion (RD) level set method and the obtained results are validated against the Ground Truth (GT) images. Ten LB eigen values are extracted from the segmented CC images. LB eigen values are positive sequence of infinite series that describe the intrinsic geometry of objects. These values capture the shape information of CC by solving the eigen value problem of LB operator on the triangular meshes. The significant features are selected based on Information Gain (IG) ranking and subjected to classification using K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Naive Bayes (NB). The performance of LB eigen values in the AD diagnosis is evaluated using classifiers' accuracy, specificity and sensitivity measures.Results show that, RD level set is able to segment CC in normal and AD images with high percentage of similarity with GT. The extracted LB eigen values are found to show high difference in the mean values between normal and AD subjects with high statistical significance. The LB eigen modes λ2, λ7 and λ8 are identified as prominent features by IG based ranking. KNN is able to give maximum classification accuracy of 93.37% compared to linear SVM and NB classifiers. This value is observed to be high than the results obtained using geometric features. The proposed CAD system focuses solely on the geometric variations of CC extracted using LB eigen value spectrum. The extraction of eigen modes in the LB spectrum is easy to compute, does not involve too many parameters and less time consuming. Thus this CAD study seems to be clinically significant in the shape investigation of brain structures for AD diagnosis.
40 citations
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TL;DR: A novel approach to edge-aware image manipulation that processes a Gaussian pyramid from coarse to fine, and at each level, applies a nonlinear filter bank to the neighborhood of each pixel using an explicit mixed-domain solution.
Abstract: This paper presents a novel approach to edge-aware image manipulation. Our method processes a Gaussian pyramid from coarse to fine, and at each level, applies a nonlinear filter bank to the neighborhood of each pixel. Outputs of these spatially-varying filters are merged using global optimization. The optimization problem is solved using an explicit mixed-domain (real space and DCT transform space) solution, which is efficient, accurate, and easy-to-implement. We demonstrate applications of our method to a set of problems, including detail and contrast manipulation, HDR compression, nonphotorealistic rendering, and haze removal.
38 citations
"Binary Grey Wolf Optimizer based Fe..." refers background or methods in this paper
...Edge aware local contrast manipulation modifies the image contrast by retaining the edge information [7]....
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...Several methods such as anisotropic diffusion, domain transform and bilateral filtering are reported in the literature [7]....
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