Hybrid Ensemble Classifier Research Articles

Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier

Chronic liver disease (CLD), which indicates the inflammatory condition of the liver, leads to cirrhosis or even partial or total liver dysfunction when left untreated. A non-invasive approach for evaluating CLD with computed tomography (CT) images is proposed using an ensemble of classifiers. To accurately classify CLD, the hybrid whale optimization algorithm with simulated annealing (WOA-SA) is used in selecting an optimal set of features. The proposed method employs seven sets of features with a total of 73–3D (three-dimensional) texture features. A hybrid ensemble classifier with support vector machine (SVM), k—Nearest Neighbor (k-NN), and random forest (RF) classifiers are used to classify liver diseases. Experimental analysis is performed on clinical CT images datasets, which include normal liver, fatty liver, metastasis, cirrhosis, and cancerous samples. The optimal features selected using the WOA-SA improve the accuracy of CLD classification for the five classes of diseases mentioned above. The accuracy of the liver classification using ensemble classifier yields approximately 98% with a 95% confidence interval (CI) of (0.7789, 1.0000) and an error rate of 1.9%. The performance of the proposed method is compared with two existing algorithms and the sensitivity and specificity yield an overall average of 96% and 93%, with 95% confidence interval of (0.7513, 1.0000) and (0.7126, 1.0000), respectively. Classification of CLD based on ensemble classifier illustrates the effectiveness of the proposed method and the comparison analysis demonstrates the superiority of the methodology.

A Two Stage Hybrid Ensemble Classifier Based Diagnostic Tool for Chronic Kidney Disease Diagnosis Using Optimally Selected Reduced Feature Set

Objective: This paper presents an idea of applying a two stage hybrid ensemble classifier for improving the prediction accuracy of Machine Learning based automated diagnosis of chronic kidney disease on the basis of values of an optimally selected subset of clinical and physiological parameters fed to it. Methodology: Chronic kidney disease is a generalized term for various heterogeneous disorders affecting the structure and function of the kidney. It is a disease with high mortality rate. In this paper the authors have proposed a two stage hybrid ensemble technique with very high efficiency. In two stage hybrid ensemble classifier the potential of individual classification algorithms are combined together. In addition to this the authors optimally selected 8 parameters of prime importance from the set of 24 parameters of the dataset used for the study .The parameters (features) selected represent the intersection of the two sets; one containing medically essential parameters arranged in decreasing contribution to the diagnosis and other set containing parameters ranked in decreasing order of their contribution in the Machine Learning classification process. Results: The results depict that the two stage hybrid ensemble is a very efficient method for classification of chronic kidney disease. The results of this ensemble classifier on the optimally selected reduced feature set (with 8 parameters) as well as the complete feature set (with 24 parameters) in terms of various performance metrics are predictive accuracy of (2-class) 100%, sensitivity of 1, precision of 1, specificity of 1 and F-value of 1. Conclusion : The GUI based diagnostic tool developed on the basis of the proposed ensemble can act as a tool for assisting doctors for cross-validating their findings of initial screening of chronic kidney disease using fewer clinical parameters thus helping them to attend to the needs of more patients in less time.

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier

Background and objectivesThe purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. MethodsPixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. ResultsOur experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. ConclusionsThe proposed method had high clinical applicability for image-based tissue characterization.

Optimizing the Prediction of Bagging and Boosting

Background/Objectives: Since more than a decade, Ensemble methods like Bagging and Boosting have drawn great attention by the researchers aiming to improve the prediction accuracy over single classifiers Despite Some recent studies have noticed that Bagging and Boosting does not always improve the accuracy, it enhances the accuracy only if the classifier is unstable classifier. To overcome this problem, a Hybrid Ensemble Model with two phases of preprocessing is proposed in this paper and evaluated using 9 classifiers on 3 benchmark data sets of UCI Repository. Methods: In the first phase of preprocessing feature selection is performed using CFS to select the attributes highly correlated to the class and in the second phase K-means clustering algorithm is applied to remove the noisy instances. Finally, the resultant instances from the previous stages are trained with Bagging and Boosting ensembles to build the final Hybrid Ensemble classifier Model (HECM) using 10 fold cross validation. The result was evaluated using the confusion matrix and the performance measures like accuracy, kappa, mean absolute error and time to build the model. Findings: Results proved that proposed model is more efficient than the existing models and showed improved accuracy for both stable and unstable classifier ranging from 2% to 30.14% over traditional ensemble model depending upon the complexity of the algorithm.

Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis

The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Breast cancer is becoming a leading cause of death among women in the whole world; meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This paper presents a hybrid intelligent system for recognition of breast cancer tumors. The proposed system includes two main modules: the feature extraction module and the predictor module. In the feature extraction module, rough set theory is used to preprocess the attributes on condition that the important information is not lost, deletes redundant attributes and conflicting objects from decision table. In the predictor module, a combined classifier is proposed based on K-nearest neighbor classifier. Experiments have been conducted on a widely used Wisconsin breast cancer dataset taken from University of California Irvine. Experimental results show that the proposed hybrid system can improve the rate of correct diagnosis of cases. The proposed combined classifier with rough set-based feature selection achieves 99.41 % classification accuracy and uses only 4 features which is the best shown to date. Different performance metrics are used to show the effectiveness of the proposed hybrid system. With these results, the proposed method is very promising compared to the previously reported results and can be used confidently for other breast cancer diagnosis problems.

A design heuristic for hybrid classification ensembles in machine learning

This paper presents a new design heuristic for hybrid classifier ensembles in machine learning. The heuristic entails inclusion of both global and local learners in the composition of base classifiers of a hybrid classifier ensemble, while also inducing both heterogeneous and homogenous diversity through the co-existence of global and local learners. Realization of the proposed heuristic is demonstrated within a hybrid ensemble classifier framework. The utility of proposed heuristic for enhancing the hybrid classifier ensemble performance is assessed and evaluated through a simulation study. Weka machine learning tool bench along with 46 datasets from the UCI machine learning repository are used. Simulation results indicate that the proposed heuristic enhances the performance of a hybrid classification ensemble.