Max Neural Network Research Articles

A modified neuro-fuzzy classifier and its parallel implementation on modern GPUs for real time intrusion detection

According to the Cisco Visual Networking Index, the annual global network traffic is growing consistently and would reach 3.3 ZB by 2021. The percentage of attack traffic is also increasing with the same rate in network traffic. The rapidly growing unauthorized activities on a network need a time efficient intrusion detection system with the high detection rate of attacks. Here, we propose a modification in the recall phase of a well-known method, enhanced fuzzy min–max neural network (EFMN), with the aim to achieve high detection rate of attacks in the minimum training and recall time. The modified-EFMN is employed for the detection of network attacks and the results are compared with the standard SVM, rule-based, and neuro-fuzzy-based classifiers on the benchmark datasets. We demonstrate that the proposed method provides a time efficient intrusion detection with the competitive detection rate. We also discuss the parallel implementation of the modified-EFMN on the modern Graphics Processing Units (GPUs) to accelerate its performance further for the heavily growing network traffic. The NVIDIA GeForce GTX 750 Ti with 640 cores is used for the implementation.

A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification

Swarm intelligence (SI)-based optimization methods have been extensively used to tackle feature selection problems. A feature selection method extracts the most significant features and removes irrelevant ones from the data set, in order to reduce feature dimensionality and improve the classification accuracy. This paper combines the incremental learning Fuzzy Min–Max (FMM) neural network and Brain Storm Optimization (BSO) to undertake feature selection and classification problems. Firstly, FMM is used to create a number of hyperboxes incrementally. BSO, which is inspired by the human brainstorming process, is then employed to search for an optimal feature subset. Ten benchmark problems and a real-world case study are conducted to evaluate the effectiveness of the proposed FMM-BSO. In addition, the bootstrap method with the 95% confidence intervals is used to quantify the results statistically. The experimental results indicate that FMM-BSO is able to produce promising results as compared with those from the original FMM network and other state-of-the-art feature selection methods such as particle swarm optimization, genetic algorithm, and ant lion optimization.

A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification

At present, pattern classification is one of the most important aspects of establishing machine intelligence systems for tackling decision-making processes. The fuzzy min–max (FMM) neural network combines the operations of an artificial neural network and fuzzy set theory into a common framework. FMM is considered one of the most useful neural networks for pattern classification. This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement. The review is conducted based on a methodical protocol. Through a rigorous searching and filtering process, the relevant studies are extracted and comprehensively analyzed to adequately address the defined research questions. The findings indicate that FMM plays a critical role in providing solutions to pattern classification issues. The FMM model and a number of FMM-based variants are identified and systematically analyzed with respect to their aims, improvements introduced and results achieved. In addition, FMM and its variants are critically analyzed with respect to their benefits and limitations. This paper shows that the existing FMM-based variants still encounter issues in terms of the learning process (expansion, overlap test, and contraction), which influence the classification performance. Based on the review findings, research opportunities are suggested to propose a new model to enhance the number of existing FMM models, particularly in terms of their learning process by minimizing hyperbox overlap pertaining to different classes as well as avoiding membership ambiguity of the overlapped region. In short, this review provides a comprehensive and critical reference for researchers and practitioners to leverage FMM and its variants for undertaking pattern classification tasks.

Survey of Fuzzy Min–Max Neural Network for Pattern Classification Variants and Applications

Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many different real-world applications. Artificial neural networks and fuzzy logic are two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the hybrid models, the fuzzy min–max (FMM) neural network has been proven to be a premier model for undertaking pattern classification problems. While FMM is useful in terms of its capability of online learning, it suffers from several limitations in the learning procedure. Therefore, over the past years, researchers have proposed numerous improvements to overcome the limitations of the original FMM model. This paper carries out a comprehensive survey of the developments conducted on the FMM model for pattern classification. In order to assist recent researchers in selecting the most suitable FMM variant and to provide proper guidance for future developments, this study divides the variants of FMM into two main board categories, namely FMM variants with and without contraction. This division facilitates understanding of the developments conducted by researchers on the original FMM neural network, as well as provides the scope to identify the limitations that still exist in the FMM models. This paper also summarizes the use of FMM and its variants in solving different benchmark and real-world problems. Finally, the possible future trends are highlighted.

The Combination of Fuzzy Min–Max Neural Network and Semi-supervised Learning in Solving Liver Disease Diagnosis Support Problem

The novel model namely semi-supervised clustering in the fuzzy min–max neural network is proposed. This model is based on fuzzy min–max neural network and semi-supervised learning method. This model is able to consider as a binary classifier in order to determine an input sample affected the liver disease or not. The proposed model is implemented on a real data including 4.156 samples of patients from Gang Thep Hospital and Thai Nguyen National Hospital and four other datasets from UCI. In this method, all input samples are unlabeled samples. Thus, the expense of labeling the data is omitted. This means that the cost of diagnosis progress from collecting data to making decision is low. Experimental results show that the performance of the proposed model on datasets is higher than other compared ones.

Improving the Fuzzy Min–Max neural network performance with an ensemble of clustering trees

Abstract In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min–Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is to formulate an accurate and useful learning model that is able to perform online clustering and to explain its predictions. The online clustering capability is inherited from the FMM hyperboxes, while the explanatory capability arises from the underlying decision trees of ECT. Four different mean measures, namely harmonic, geometric, arithmetic, and root mean square, are incorporated into FMM for computing its hyperbox centroids. A series of benchmark and real-world data sets are used for evaluating the FMM-ECT performance. The results are analyzed and compared with those from other models. The outcomes indicate that FMM-ECT is able to achieve comparable clustering performances, with the advantage of providing explanations of its predictions using a decision tree.

Extended fuzzy hyperline-segment neural network with classification rule extraction

The hyperline segment layer of fuzzy hyperline segment neural network (FHLSNN) consists of number of n- dimensional hyperline segments. Each hyperline segment has the two end points defined in terms of continuous attribute values and an associated membership function. The membership function processes the continuous attribute values and gives the membership of each input pattern to the hyperline segments. These end points and membership function are defined only in terms of continuous attributes and cannot process discrete attributes. But the real world data comprises both continuous and discrete attributes. Due to this original FHLSNN and its ancestor versions cannot be applied to these types of data. Also, justification of the classification decision given by these fuzzy min–max neural networks is required to be obtained to make them more applicable to the real world applications. The objective of the proposed extended fuzzy hyperline segment neural network (EFHLSNN) is to solve these two problems. In the EFHLSNN, each hyperline segment has two end points defined not only in terms of continuous attributes but also the set of binary strings defined for discrete attributes. The membership function and expansion condition of the original FHLSNN are modified to process both continuous and discrete attribute values. The proposed EFHLSNN model is applied to eight different benchmark datasets. The experimental results show that the proposed model gives very good accuracy and the compact rule set that justifies the classification decision of the EFHLSNN and thus, giving rise to good rule fidelity.

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

An enhanced fuzzy min–max neural network with ant colony optimization based-rule-extractor for decision making

This paper proposes an enhanced fuzzy min–max neural (EFMN) network model with ant colony optimization based-rule-extractor for grouping of the data-patterns and decision making by rule-list. There are many methods to extract the rules which are having less accuracy and with less performance. The earlier methods have drawbacks like they have not maintained the rule accuracy in terms of consistency. The proposed method has improved the accuracy, consistency and performance against existing methods. The number of rules obtained from this system is less in count and having higher rank. One of the strength of this method is that the rules obtained from the system are comprehensible, as they are in rule list format. This rule list is useful in decision making problem.

A modified fuzzy min–max neural network for data clustering and its application on pipeline internal inspection data

In this paper, a modified fuzzy min–max neural network (MFMC) for data clustering is proposed. In MFMC, the centroid information, the similarity and the noise of data are taken into the consideration. What’s more, the hyperbox entropy (HE) is first introduced to evaluate the performance of each hyperbox when doing the contraction process. In addition, in order to test the performance of the MFMC model, a series of simulations on benchmark data sets are conducted. Then a real-world application study on the pipeline internal inspection data is also performed. The experimental result indicates that the MFMC has more excellent performance than other existed fuzzy min–max clustering algorithms.

A classification of chronic leukaemia using new extension of k-means clustering and EFMM based on digital microscopic blood images

Leukaemia is a cancer of the white blood cells. The type of white blood cell affected in either lymphoid or myeloid. And leukaemia is defined in two ways, such as acute leukaemia (AL) and chronic leukaemia (CL). These kinds of leukaemia start when typical blood cells change and grow wildly. This paper describes in the following steps to classify the chronic leukaemia automatically and more accurately. First, pre-processing the colour scale of digital microscope blood image, then segment the image by new extension of k-means clustering algorithm, and Hausdorff dimension (HD) is utilised for feature extraction, finally the classification is done by utilising Enhanced Fuzzy Min Max (EFMM) neural network. The proposed method obtained 99.95% accuracy for Lymphocytic and Myelogenous cells.

A classification of chronic leukaemia using new extension of k-means clustering and EFMM based on digital microscopic blood images

Leukaemia is a cancer of the white blood cells. The type of white blood cell affected in either lymphoid or myeloid. And leukaemia is defined in two ways, such as acute leukaemia (AL) and chronic leukaemia (CL). These kinds of leukaemia start when typical blood cells change and grow wildly. This paper describes in the following steps to classify the chronic leukaemia automatically and more accurately. First, pre-processing the colour scale of digital microscope blood image, then segment the image by new extension of k-means clustering algorithm, and Hausdorff dimension (HD) is utilised for feature extraction, finally the classification is done by utilising Enhanced Fuzzy Min Max (EFMM) neural network. The proposed method obtained 99.95% accuracy for Lymphocytic and Myelogenous cells.

A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min–max neural network

In this paper, we extend our previous work on the Enhanced Fuzzy Min–Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems.

Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.

Fuzzy min–max neural network and particle swarm optimization based intrusion detection system

To maintain the integrity, availability, reliability of the data and services available on web requires a strong network security framework, in such consequence IDS based on data mining are the best solution. In this paper we proposed an intrusion detection system which is based on the fuzzy min max neural network and the particle swarm optimization. The proposed system is tested with the help of preprocessed KDD CUP data set. Classification accuracy and classification error are taken as a performance evaluation parameter to test the effectiveness of the system. The proposed system is compared with the some of the well-known methods, the results shows that the proposed system performed well as compared to the other systems.

Power Quality Analysis Using a Hybrid Model of the Fuzzy Min-Max Neural Network and Clustering Tree.

A hybrid intelligent model comprising a modified fuzzy min-max (FMM) clustering neural network and a modified clustering tree (CT) is developed. A review of clustering models with rule extraction capabilities is presented. The hybrid FMM-CT model is explained. We first use several benchmark problems to illustrate the cluster evolution patterns from the proposed modifications in FMM. Then, we employ a case study with real data related to power quality monitoring to assess the usefulness of FMM-CT. The results are compared with those from other clustering models. More importantly, we extract explanatory rules from FMM-CT to justify its predictions. The empirical findings indicate the usefulness of the proposed model in tackling data clustering and power quality monitoring problems under different environments.

Extracting classification rules from modified fuzzy min–max neural network for data with mixed attributes

This paper proposes the modified fuzzy min–max neural network (MFMMN) classification model to perform the supervised classification of data. The basic fuzzy min–max neural network (FMMN) can only be applied to the continuous attribute values and cannot handle the discrete values. Also justification of the classification results given by FMMN required to be obtained to make it more applicable to real world applications. These both issues are solved in the proposed MFMMN. In the MFMMN, each hyperbox have min–max values defined in terms of continuous attributes and a set of binary strings defined for discrete attributes. Bitwise ‘and’ and ‘or’ operators are used to update the discrete values associated with each hyperbox. The trained network is pruned to remove the less useful hyperboxes based on their confidence factor. The proposed model is applied to nine different datasets taken from the University of California, Irvine (UCI) machine learning repository. Finally the case study of a real time weather data is evaluated using MFMMN. The experimental results show that the proposed model has given very good accuracy. In addition to accuracy, the number of hyperboxes obtained after pruning are very less which lead to less number of concise rules and reduced computational complexity.

An enhanced fuzzy min-max neural network for pattern classification.

An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from various FMM-based models, support vector machine-based, Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.

A modified fuzzy min–max neural network for data clustering and its application to power quality monitoring

When no prior knowledge is available, clustering is a useful technique for categorizing data into meaningful groups or clusters. In this paper, a modified fuzzy min–max (MFMM) clustering neural network is proposed. Its efficacy for tackling power quality monitoring tasks is demonstrated. A literature review on various clustering techniques is first presented. To evaluate the proposed MFMM model, a performance comparison study using benchmark data sets pertaining to clustering problems is conducted. The results obtained are comparable with those reported in the literature. Then, a real-world case study on power quality monitoring tasks is performed. The results are compared with those from the fuzzy c-means and k-means clustering methods. The experimental outcome positively indicates the potential of MFMM in undertaking data clustering tasks and its applicability to the power systems domain.