Neural Network For Data Classification Research Articles

ДОСЛІДЖЕННЯ ІНТЕЛЕКТУАЛЬНИХ МОДЕЛЕЙ КЛАСИФІКАЦІЇ НЕВИЗНАЧЕНИХ ДАНИХ З ВИМОГАМИ ДОСТОВІРНОСТІ РЕЗУЛЬТАТІВ

The report contains the results of research and development of intellectual models for managing complex systems under conditions of data uncertainty based on classification procedures, which ensure reliable solution of tasks considering the assessment of the boundary dimensionality of models. The possibilities of improving Hamming neural networks for data classification in formats of fuzzy variables and certainty factor CF(A) are investigated. The features of the mathematical model of classification tasks based on a set of feature templates are determined. A software complex of information technology for assignment/selection of performers, as well as determination of authorship of Ukrainian-language works based on classification of sets of templates with certain fuzzy features, is presented. The software complex utilizes the reduction and Cohen's kappa statistics procedures proposed in the report.

Fractional cuckoo search invasive weed optimized neural network for data classification

SummaryThe progression of organizing and classifying data components based on pre‐established criteria is known as data classification. In data classification problems, machine learning methods are frequently used. Although the ability of an algorithm to learn is essential, the recognition quality of algorithms is undoubtedly a key indicator. However, the error made in the beginning is significant, and if it is not rectified then, it causes chaos in data classification. In this research, a fractional hybrid optimization is created with a feed‐forward neural network (FFNN) for the data classification. The pre‐processing of the medical data involves the imputation of missing values and the log transformation. In the feature selection process, the chord distance and the Jaro‐Winkler distance are both employed to choose the important features. The FFNN classifier is used to classify the data, which is trained using the hybrid optimization method, namely the developed Fractional Cuckoo search Invasive Weed Optimization algorithm (FCSIWO). The fractional concept, Invasive Weed Optimization (IWO) algorithm, and Cuckoo search (CS) algorithm are combined to create the FCSIWO. When compared to existing data classification models, the developed technique achieved an optimal sensitivity of 0.9463, an optimal accuracy of 0.9440, and an optimal specificity of 0.9422.

Methods for Constructing Artificial Neural Networks for Data Classification

The features of the organization of distance learning of students in a higher educational institution, as well as the information and educational technologies necessary for this, are considered. A system of automatic assessment of students’ knowledge is proposed. It is based on a model in the form of an artificial neural network. The features of such a model are given. The two implemented methods for constructing artificial neural networks have been used in the software module for testing students’ knowledge. The choice of the type of network, its structure, and parameters has been substantiated. The first method is related to the construction of an artificial neural network in the manual mode. An algorithm is presented that reflects the iterative process of its training. In the second case, the network is built automatically by applying a genetic algorithm. At the beginning of the work, a set of randomly generated initial data arrives at the input of the algorithm. In the course of its work, the genetic algorithm determines the architecture and parameters of the neural network, which ensure the successful solution of the assigned applied problem. Trained networks are used to classify data. Both networks showed acceptable classification accuracy of the results obtained in the course of the students’ knowledge testing.

A semisupervised learning model based on fuzzy min–max neural networks for data classification

Semisupervised learning (SSL) models are useful for undertaking classification problems with a small set of labeled samples and a large number of unlabeled samples. In this regard, the family of fuzzy min–max (FMM) neural networks offers the capability of online learning for addressing both unsupervised and supervised problems. As such, this paper proposes a novel two-stage SSL model based on FMM networks, denoted SSL–FMM. The first stage employs the unlabeled samples to generate a number of hyperboxes using the unsupervised FMM algorithm, while the second stage uses the labeled samples to associate the generated hyperboxes with their target classes using the supervised FMM algorithm. In addition, a neighborhood-labeling mechanism based on the Euclidean distance and hyperbox centroids is formulated to associate the unlabeled hyperboxes with the most likely target classes. A number of benchmark problems and a real-world case study are employed to evaluate the effectiveness of the proposed SSL–FMM model. The outcome indicates that SSL–FMM is able to use unlabeled samples effectively and improve the FMM performance, producing promising results compared with other SSL methods in the literature.

A Novel Quantum-Inspired Fuzzy Based Neural Network for Data Classification

The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> , which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.

Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based on Lévy flight

Within neural networks, it is considered a difficult task to find optimal values for the number of hidden neurons and connection weights simultaneously. This is because altering the hidden neurons significantly affects a neural network’s structure and increases the difficulty of the training process which needs special considerations. Particle swarm optimization (PSO) is one of the most important metaheuristic algorithms due to its convergence speed and its simplicity of implementation. Multi-verse optimization (MVO) based on Levy flight is a recent and fast algorithm and can avoid premature convergence and can achieve a better balance between exploration and exploitation. This paper presents a new training method based on hybrid particle swarm optimization with Multi-verse optimization based on Levy flight (PLMVO) to optimize the number of hidden neurons and connection weights simultaneously in feedforward neural networks (FFNN). The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. To evaluate the proposed algorithm we used three experimental series. In the first one, the proposed PLMVO algorithm is compared with the MVO and PSO algorithms to solve a set of 15 benchmark functions to find the global solution. Meanwhile, in the second experiment, the performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using nine bio-medical datasets. The results of the comparative study show that PLMVO outperformed other training methods in most datasets and can be an alternative to other training methods. In the third experiment, the proposed PLMVO-MLP is used to predict malicious executable Linux files. The implemented model achieved very promising results with very high accuracy of 1.0, and an average f-measure of 1.0.

Coupled-View Deep Classifier Learning from Multiple Noisy Annotators

Typically, learning a deep classifier from massive cleanly annotated instances is effective but impractical in many real-world scenarios. An alternative is collecting and aggregating multiple noisy annotations for each instance to train the classifier. Inspired by that, this paper proposes to learn deep classifier from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classification and the learning view from labels is described by a Naive Bayes classifier for label aggregation. Such coupled-view learning is converted to a supervised learning problem under the mutual supervision of the aggregated and predicted labels, and can be solved via alternate optimization to update labels and refine the classifiers. To alleviate the propagation of incorrect labels, small-loss metric is proposed to select reliable instances in both views. A co-teaching strategy with class-weighted loss is further leveraged in the deep classifier learning, which uses two networks with different learning abilities to teach each other, and the diverse errors introduced by noisy labels can be filtered out by peer networks. By these strategies, our approach can finally learn a robust data classifier which less overfits to label noise. Experimental results on synthetic and real data demonstrate the effectiveness and robustness of the proposed approach.

Semi-supervised Fuzzy Min–Max Neural Network for Data Classification

Learning from the lack of labeled data is a challenging task which often limits the performance of the classifier. Since the unlabeled data is easy to obtain, using both of the labeled and unlabeled data in the training process provide a way to solve this problem. In this paper, a semi-supervised classification method based on fuzzy min–max neural network (SS-FMM) is proposed. In SS-FMM, the network has been modified for handling both of the labeled and unlabeled data. In addition, the staged feedback process is designed to modify the network structure of the traditional fuzzy min–max neural network. A staged-threshold function designed in SS-FMM, the hyperbox pruning process and the hyperbox relabeling process can be started dynamically. Moreover, the hyperboxes relabeling process and the hyperbox pruning process are designed to maximize using the unlabeled data and control the amount of the hyperboxes. In order to testify the effectiveness of SS-FMM, various experiments are carried out with several benchmark data sets. In addition, SS-FMM has been applied on the internal inspection data of our system. The results show that SS-FMMM has got good performance.

An Improved Fuzzy Min–Max Neural Network for Data Classification

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min–max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k -nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k- nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.

Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search

Artificial Neural networks (ANNs) are often applied to data classification problems. However, training ANNs remains a challenging task due to the large and high dimensional nature of search space particularly in the process of fine-tuning the best set of control parameters in terms of weight and bias. Evolutionary algorithms are proved to be a reliable optimization method for training the parameters. While a number of conventional training algorithms have been proposed and applied to various applications, most of them share the common disadvantages of local optima stagnation and slow convergence. In this paper, we propose a new evolutionary training algorithm referred to as LPSONS, which combines the velocity operators in Particle Swarm Optimization (PSO) with Mantegna Lévy distribution to produce more diverse solutions by dividing the population and generation between different sections of the algorithm. It further combines Neighborhood Search with Mantegna Lévy distribution to mitigate premature convergence and avoid local minima. The proposed algorithm can find optimal results and at the same time avoid stagnation in local optimum solutions as well as prevent premature convergence in training Feedforward Multi-Layer Perceptron (MLP) ANNs. Experiments with fourteen standard datasets from UCI machine learning repository confirm that the LPSONS algorithm significantly outperforms a gradient-based approach as well as some well-known evolutionary algorithms that are also based on enhancing PSO.

An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification

An ensemble of Enhanced Fuzzy Min Max (EFMM) neural networks for data classification is proposed in this paper. The certified belief in strength (CBS) method is used to formulate the ensemble EFMM model, with the aim to improve the performance of individual EFMM networks. The CBS method is used to measure trustworthiness of each individual EFMM network based on its reputation and strength indicators. Trust is built from strong elements associated with the EFMM network, allowing the CBS method to improve the performance of the ensemble model. An auction procedure based on the first-price sealed-bid scheme is adopted for determining the winning EFMM network in undertaking classification tasks. The effectiveness of the ensemble model is demonstrated using a number of benchmark data sets. Comparing with the existing EFMM networks, the proposed ensemble model is able to improve classification accuracy rates in the empirical study.

An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification

An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification

Intra-specific competitive co-evolutionary artificial neural network for data classification

This paper presents an alternative approach of competitive co-evolutionary (ComCoE) artificial neural network (ANN) developed for data classification. The motivation of this work is to employ an interactive game-based fitness evaluation method within a CoE framework to develop a compact and accurate ANN model. The proposed model uses only one population of radial basis function artificial neural networks (RBFANNs) in the CoE framework to find out an optimised RBFANN. In the ComCoE process, the RBFANNs compete in an intra-specific competition environment, which is driven by a game-based fitness evaluation method. The fitness evaluation for each RBFANN is made by computing the interaction among the selected RBFANNs in a population quantitatively throughout a number of encounters under a Single Elimination Tournament topology. Two indicators, i.e. the classification accuracy and hidden nodes number of each RBFANN, are referred to compute the fitness value. The proposed model performs a global search for finding potential near optimal solution. Then, a local search (Backpropagation algorithm) is executed to reach at a precise solution. The proposed classification model is evaluated using 14 public data sets from the UCI machine-learning repository. A performance comparison between the proposed model and other state-of-art classifiers is also conducted. The empirical results show that the proposed model, which constructs a compact network structure, could perform with high classification accuracy rates.

Novel weighting in single hidden layer feedforward neural networks for data classification

We propose a binary classifier based on the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden layer. We use a modified attribute-class correlation measure to determine the weights of attributes in the networks. Moreover, we propose new weights called as influence weights to utilize in the weights connecting the input layer and the hidden layer nodes (hidden weights) of the network with sigmoid hidden nodes. These weights are calculated as the sum of conditional probabilities of attribute values given class labels. Our learning procedure of the networks is based on the extreme learning machines; in which the parameters of the hidden nodes are first calculated and then the weights connecting the hidden nodes and output nodes (output weights) are found. The results of the networks with the proposed weights on some benchmark data sets show improvements over those of the conventional networks.

Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons

Central Force Optimization (CFO) is a novel and upcoming metaheuristic technique that is based upon physical kinematics. It has previously been demonstrated that CFO is effective when compared with other metaheuristic techniques when applied to multiple benchmark problems and some real world applications. This work applies the CFO algorithm to training neural networks for data classification. As a proof of concept, the CFO algorithm is first applied to train a basic neural network that represents the logical XOR function. This work is then extended to train two different neural networks in order to properly classify members of the Iris data set. These results are compared and contrasted to results gathered using Particle Swarm Optimization (PSO) in the same applications. Similarities and differences between CFO and PSO are also explored in the areas of algorithm design, computational complexity, and natural basis. The paper concludes that CFO is a novel and promising meta-heuristic that is competitive with if not superior to the PSO algorithm, and there is much room to further improve it.

A method for creating fuzzy neural-network models using the MATLAB package for biomedical applications

Problems of MATLAB-based simulation of hybrid neural networks for data classification in biomedical applications are considered. The neural-network structure is used as a defuzzifier. Graphic interfaces for constructing fuzzy neural-network models are suggested. Methods for merging models constructed using different MATLAB packages and an algorithm for constructing a fuzzy neural-network model are suggested. The algorithm includes both modules for tuning membership functions of the fuzzy system fuzzifier, and modules for tuning the parameters of the defuzzifier implemented as a two-layer neural-network structure of the perceptron type.

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.