Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis

The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to  show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm

Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis

The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm

A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN) based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature

An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning

Real-world classification datasets often present a skewed distribution of patterns, where one or more classes are under-represented with respect to the rest. One of the most successful approaches for alleviating this problem is the generation of synthetic minority samples by convex combination of available ones. Within this framework, adaptive synthetic (ADASYN) sampling is a relatively new method which imposes weights on minority examples according to their learning complexity, in such a way that difficult examples are more prone to be oversampled. This paper proposes an improvement of the ADASYN method, where the learning complexity of these patterns is also used to decide which sample of the neighbourhood is selected. Moreover, to avoid suboptimal results when performing the random convex combination, this paper explores the application of an iterative greedy algorithm which refines the synthetic patterns by repeatedly replacing a part of them. For the experiments, six binary datasets and four over-sampling methods are considered. The results show that the new version of ADASYN leads to more robust results and that the application of the iterative greedy metaheuristic significantly improves the quality of the generated patterns, presenting a positive effect on the final classification model

Optimal configuration of hybrid AC/DC urban distribution networks for high penetration renewable energy

Existing AC medium-voltage distribution networks are facing challenges on handling increasing loads and renewable energy integrations. However, it is very difficult to build new distribution lines in urban areas. This study proposes a configuration method of hybrid AC/DC medium-voltage distribution networks, in which some existing AC lines are converted to DC operation. Existing topologies and dispatching scenarios are considered during configuration because the overall power flow can be rescheduled in the hybrid AC/DC distribution network. Therefore, transfer capacities of the lines are fully utilised, and more renewable energies are accommodated. A bi-level programming model is established embedding chance constraint programming to consider the intermittent output of renewable energy. In the upper level, a multiple objective optimal model is proposed in order to balance investments, power losses, and the maximum load level and renewable energy capacity. In the lower level, daily operations of the newly installed VSCs are optimised by a chance constraint programming. The influences of energy storage systems on the configuration are also analysed. Simulation studies are performed to verify the proposed method

Coordinated voltage regulation of hybrid AC/DC medium voltage distribution networks

Abstract In a hybrid AC/DC medium voltage distribution network, distributed generations (DGs), energy storage systems (ESSs), and the voltage source converters (VSCs) between AC and DC lines, have the ability to regulate node voltages in real-time. However, the voltage regulation abilities of above devices are limited by their ratings. And the voltage regulation efficiencies of these devices are also different. Besides, due to high r/x ratio, node voltages are influenced by both real and reactive power. In order to achieve the coordinated voltage regulation in a hybrid AC/DC distribution network, a priority-based real-time control strategy is proposed based on the voltage control effect of real and reactive power adjustment. The equivalence of real and reactive power adjustment on voltage control is considered in control area partition optimization, in which regulation efficiency and capability are taken as objectives. In order to accommodate more DGs, the coordination of controllable devices is achieved according to voltage sensitivities. Simulations studies are performed to verify the proposed method

Hybrib NSGA-II optimization for improving the three-term BP network for multiclass classification problems

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions.The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions.The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems.Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in whichthese arm movements are detected during an archetypal activity of daily-living (ADL) – ‘making-a-cup-of-tea’. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results showthat the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology

Machine learning methods for binary and multiclass classification of melanoma thickness From dermoscopic images

Thickness of the melanoma is the most important factor associated with survival in patients with melanoma. It is most commonly reported as a measurement of depth given in millimeters (mm) and computed by means of pathological examination after a biopsy of the suspected lesion. In order to avoid the use of an invasive method in the estimation of the thickness of melanoma before surgery, we propose a computational image analysis system from dermoscopic images. The proposed feature extraction is based on the clinical findings that correlate certain characteristics present in dermoscopic images and tumor depth. Two supervised classification schemes are proposed: a binary classification in which melanomas are classified into thin or thick, and a three-class scheme (thin, intermediate, and thick). The performance of several nominal classification methods, including a recent interpretable method combining logistic regression with artificial neural networks (Logistic regression using Initial variables and Product Units, LIPU), is compared. For the three-class problem, a set of ordinal classification methods (considering ordering relation between the three classes) is included. For the binary case, LIPU outperforms all the other methods with an accuracy of 77.6%, while, for the second scheme, although LIPU reports the highest overall accuracy, the ordinal classification methods achieve a better balance between the performances of all classes