Fuzzy ARTMAP Research Articles

Sugarcane mapping in Paraná State Brazil using MODIS EVI images

Sugarcane cultivated in Brazil deserves attention because it makes the Country the world's largest producer of sugar and ethanol. The aim of this work was to develop and evaluate a methodology for sugarcane mapping in Parana State, Brazil using temporal series of the MODIS EVI, for 2010/2011 to 2013/2014 crop seasons. The methodology included supervised classification Fuzzy ARTMAP, taking as input variables such as terms of harmonics amplitude and phase, and phenological metrics of culture. Area estimates indicated a moderate and strong correlation (rs), ranging from 0.62 to 0.71 comparing with IBGE official data and from 0.79 to 0.87 with the Canasat data. To assess mapping accuracy, Canasat vector maps were used as reference to build the confusion matrix. The method developed based on Fuzzy ARTMAP proved efficient to map and estimate the acreage of sugarcane in the State of Parana, due to digital processing techniques used in homogeneous samples, selection of phenological seasonal metrics, and decomposition of images in accordance with harmonics and supervised training. These together minimized the neural network forecast errors. Results indicate that the methodology is appropriate for sugarcane mapping. DOI: https://doi.org/10.23953/cloud.ijarsg.451

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Impact wave predictions by a Fuzzy ARTMAP neural network

Impact waves caused by landslides, rock blocks, and avalanches in lakes and dam reservoirs have provoked several disasters since the dawn of time; further, they have caused considerable number of deaths and property loss. Understanding the behavior and characteristics of these waves, principally their height, propagation velocity, and energy, are key for determining engineering parameters, and therefore developing alert systems and evacuation plans. Several studies have investigated impact waves using experimental, mathematical, and numerical models; however, few have used artificial neural networks. Considering the learning and prediction characteristics of neural networks, this work explores their application in the prediction of impact waves. The main objective is to verify the capability of a fuzzy ARTMAP neural network in predicting the evolution of the maximum wave height, one of the main parameters of impact waves. The experimental data used in this paper are from the work developed by Huber (1980) when studying the impact waves generated by dropping deformable material (granular) in a channel. This study uses different forms of normalization, as well as different training parameters. The Fuzzy ARTMAP neural network predicts adequately the evolution of the maximum wave heights, becoming useful for this kind of application.

An Overview and Recent Advances in Fuzzy ARTMAP Classifier Usage for Mapping Purposes Using Remotely Sensed Data

This paper presents an overview and recent advances on the usage of Fuzzy ARTMAP artificial neural network architecture (and its variants) for mapping purposes using remotely sensed data. It aims to offer a perspective into the past and ongoing developments of this specific research field. Moreover, this paper suggests initial pathways for those who intend to perform a scientific investigation using this artificial neural network architecture. Some applications of this architecture in other research fields are highlighted for general knowledge purposes, as well as suggestions of code repositories to implement it. Possible gaps in the literature related to Fuzzy ARTMAP classifier usage for mapping are suggested, leading to paths for future developments in this field of research.

An Approach to Enhance the Design of Protein Sequence Classifier Using Data Mining

Analysis of protein plays a major role in bio-informatics domain. Protein sequence classification is most vital in this analysis of protein. In this scenario, different researchers proposed various techniques to classify protein sequence which is reviewed here at first. Different features extraction procedure from the protein sequence is a common part of all proposed classification model. But selection and arrangement of features are the most challenging task for classifying unknown protein sequences into its known families with high accuracy and preferable computational time. Analysis of recent trend points that traditional database system fails to classify a large amount of biological data which is overcome by data mining approach. New classification model involving four phases has been proposed here for classifying the unknown sequences into its family, generating knowledge-based as well as data analysis procedure. Here a hybrid algorithm with the combination of Fuzzy ARTMAP and neural network based algorithm is used. Neighbourhood analysis is also done in the final stage. This proposed model has implemented and validated with 497 different test sequences. Finally, it is also compared with the previous model and produce high accuracy level with low computation time than previous.

Fuzzy ARTMAP driven features selection: towards a system to design information fusion for the geographic discovery

The availability of ground-level and overhead images is increasing fast. However, the potentialities of joint usage of these images on information fusion systems for in-deep geographic discovery, by answering the typical binomial quest: what and where are still to be completely evaluated. It is, therefore, necessary to establish a variety of procedures to study these potentialities including on how to assess domain-invariance between this imagery related to ground-level and overhead capture domains for example. We addressed in this paper a novel framework for one prospect for an optimal set of handcrafted domain-invariant features for ground the optimized usage of multiple sources of information on a feature level based fusion system. All of this was done framed by the usage of small datasets and, also, of interpretable classifiers (human-centered computing) and it paves the way towards the proposition of application software to be used potentially as a didactic tool with emphasis on the botanic domain. The results also endorse a domain-invariance that was already implicit on the results of previously published papers. Broadly evaluating, these results connecting image datasets on contrasting scales can launch new investigations in the scope of scale-free phenomena.

Intrusion Detection System- Via Fuzzy Artmap in Addition with Advance Semi Supervised Feature Selection

Intrusion Detection System- Via Fuzzy Artmap in Addition with Advance Semi Supervised Feature Selection

A framework to reduce category proliferation in fuzzy ARTMAP classifiers adopted for image retrieval using differential evolution algorithm

Image classifiers are largely adopted to categorize a pool of images or patterns in a databank, match category of a query image and to retrieve similar images to query from the category. Fuzzy ARTMAP (FAM) architecture have been widely included for pattern classification in various applications. The major constraint that limits the application of FAM network is category proliferation problem. That is the architecture has the tendency to increase the network size. The issue is because of noisy data, order of presenting training data and/or overlapping categories. In this paper, we propose a new methodology, DE-FAM to handle category proliferation problem by reducing the quantity of categories in the trained FAM architectures. The enhanced generalized performance, reduction in network size and influence of the proposed algorithm in computational cost is demonstrated by adopting the algorithm for image classification and retrieval. Furthermore the comparison of DE-FAM with other algorithms that address the category proliferation problem illustrate the advantages of DE-FAM.

An improved fuzzy ARTMAP and Q-learning agent model for pattern classification

The Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods.

Feature selection based on brain storm optimization for data classification

Brain storm optimization (BSO) is a new and effective swarm intelligence method inspired by the human brainstorming process. This paper presents a novel BSO-based feature selection technique for data classification. Specifically, the Fuzzy ARTMAP (FAM) model, which is employed as an incremental learning neural network, is combined with BSO, which acts as a feature selection method, to produce the hybrid FAM-BSO model for feature selection and optimization. Firstly, FAM is used to create a number of prototype nodes incrementally. Then, BSO is used to search and select an optimal sub-set of features that is able to produce high accuracy with the minimum number of features. Ten benchmark problems and a real-world case study are employed to evaluate the performance of FAM-BSO. The results are quantified statistically using the bootstrap method with the 95% confidence intervals. The outcome indicates that FAM-BSO is able to produce promising results as compared with those from original FAM and other feature selection methods including particle swarm optimization, genetic algorithm, genetic programming, and ant colony optimization.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Incremental Class Learning for Hierarchical Classification

Objects can be described in hierarchical semantics, and people also perceive them this way. It leads to the need for hierarchical classification in machine learning. On the other hand, when a new data that belongs to a new class is given, the existing classification methods should be retrained for all data including the new data. To deal with these issues, we propose an adaptive resonance theory-supervised predictive mapping for hierarchical classification (ARTMAP-HC) network that allows incremental class learning for raw data without normalization in advance. Our proposed ARTMAP-HC is composed of hierarchically stacked modules, and each module incorporates two fuzzy ARTMAP networks. Regardless of the level of the class hierarchy and the number of classes for each level, ARTMAP-HC is able to incrementally learn sequentially added input data belonging to new classes. By using a novel online normalization process, ARTMAP-HC can classify the new data without prior knowledge of the maximum value of the dataset. By adopting the prior labels appending process, the class dependency between class hierarchy levels is reflected in ARTMAP-HC. The effectiveness of the proposed ARTMAP-HC is validated through experiments on hierarchical classification datasets. To demonstrate the applicability, ARTMAP-HC is applied to a multimedia recommendation system for digital storytelling.

Open-set single-sample face recognition in video surveillance using fuzzy ARTMAP

Single-sample face recognition has been investigated by a few researches over the past few decades. However, due to the demand of identity of interest searching from video surveillance in recent years, this system has been expanded to open-set face recognition (OSFR) scheme. The OSFR system provides the identity of registered subjects and rejects the unregistered ones using only single-sample reference. This is important in video surveillance applications in which both database members and non-members are expected to appear in the scene. In this paper, we propose to use fuzzy ARTMAP neural network to solve the problem of open-set single-sample face recognition in real-world video surveillance scenario. Our proposed approach can recognize faces in near-frontal views under various illumination and facial expression conditions. Facial features are extracted using histograms of oriented gradients and Gabor wavelets and then fused using canonical correlation analysis to yield feature vectors that are robust against the aforementioned conditions. The fuzzy ARTMAP classifier has been trained using only single sample per person. We have conducted experiments on three challenging benchmark datasets, namely AR, FRGC, and ChokePoint. The experimental results have shown that the proposed approach has a superior performance than the state-of-the-art approaches.

Fault diagnosis of rolling bearing based on optimized soft competitive learning Fuzzy ART and similarity evaluation technique

In this work, a new classification method called Soft Competitive Learning Fuzzy Adaptive Resonance Theory (SFART) is proposed to diagnose bearing faults. In order to solve the misclassification caused by the traditional Fuzzy ART based on hard competitive learning, a soft competitive learning ART model is established using Yu’s norm similarity criterion and lateral inhibition theory. The proposed SFART is based on Yu’s norm similarity criterion and soft competitive learning mechanism. In SFART, Yu’s similarity criterion and the lateral inhibition theory were employed to measure the proximity and select winning neurons, respectively. To further improve the classification accuracy, a feature selection technique based on Yu’s norms is also proposed. In addition, Particle Swarm Optimization (PSO) is introduced to optimize the model parameters of SFART. Meanwhile, the validity of the feature selection technique and parameter optimization method is demonstrated. Finally, fuzzy ART/ ARTMAP (FAM) as well as the feasibility of the proposed SFART algorithm are validated by comparing the diagnosis effectiveness of the proposed algorithm with the classic Fuzzy c-means (FCM), Fuzzy ART and fuzzy ARTMAP (FAM).

A reinforced fuzzy ARTMAP model for data classification

This paper presents a hybrid model consisting of fuzzy ARTMAP (FAM) and reinforcement learning (RL) for tackling data classification problems. RL is used as a feedback mechanism to reward the prototype nodes of data samples established by FAM. Specifically, Q-learning is adopted to develop the hybrid model known as QFAM. A Q-value is assigned to each prototype node, which is updated incrementally based on the prediction accuracy of the node pertaining to each data sample. To evaluate the performance of the proposed QFAM model, a series of experiments with benchmark problems and a real-world case study, i.e., human motion recognition, are conducted. The bootstrap method is used to quantify the results with the 95% confidence interval estimates. The results are also compared with those from FAM as well as other models reported in the literature. The outcomes indicate the effectiveness of QFAM in tackling data classification tasks.

Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic

Currently, in executive comparative studies and even in the research studies of natural resources, the use of maps produced by the geological survey forms the basis of geological studies. However, in recent years, remote sensing technology has been introduced as a new and efficient tool for geological studies which in addition to the accuracy has other benefits such as access to the arduous mountainous areas or inaccessible that can help us to prepare geological maps with more accuracy. The purpose of this study is to compare geological survey geological maps with a scale of 1: 100,000, and produced and modified by Google Earth maps by using fuzzy ARTMAP artificial neural network method. For this purpose, the geological map of a part of Yazd-Shirkooh watershed prepared by using Landsat 7 image by fuzzy ARTMAP artificial neural network method with Kappa coefficient of 89%. Also, geological survey and Google Earth map prepared. Graphic map obtained from the visual interpretation of Google Earth images with the ground control was considered as the base map and comparing it with other maps evaluated by using Boolean logic. By using the sampling network created 100 points which 62 points were in the study area and 56 cases in the fuzzy ARTMAP map and 52 cases in the geological survey map were consistent with the base map. Due to reduced cost and time and no limits of time and space, fuzzy ARTMAP is a suitable method for the preparation of geological maps.

Application Study of Fuzzy ARTMAP Neural Network in Robot Servo System

Robot vision servo control is one of the hotspots in robot research field. However, it is difficult to establish an accurate mathematical model for robot vision servo system. In this paper, a robot visual servo system based on Fuzzy ARTMAP is proposed by using computer vision technology as well as fuzzy theory. After creating a mathmatic model structer, we present a simulated experiment to verify the performance of this system, which shows the superiority of Fuzzy ARTMAP by comparing it with other traditional visual servo system.

A hybrid FAM‐CART model for online data classification

AbstractIn this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM‐CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM‐CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM‐CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM‐CART model in supporting decision‐making tasks.

Object-wise joint-classification change detection for remote sensing images based on entropy query-by fuzzy ARTMAP

The pixel-wise post-classification comparison (PCC) method is widely used in remote sensing images change detection. However, it is affected by the significant cumulative error caused by single image classification error. What’s more, the pixel-wise change detection method always produces “salt and pepper” effect. To solve the excessive evaluation of changed types and quantity caused by cumulative error and “salt and pepper” effect, a novel remote sensing image change detection method called entropy query-by fuzzy ARTMAP object-wise joint classification comparison (EQFAM-OBJCC) is presented in this article. Firstly, entropy query-by measurement of active learning is integrated with the fuzzy ARTMAP neural network to choose training samples which contain large amounts of information to improve the classification accuracy. Secondly, joint classification comparison is introduced to obtain the pixel-wise classification results. Finally, the object-wise classification and change detection results are produced by superpixel segmentation method, majority voting rule, and comparison of each superpixels. Experimental results demonstrate the validity of the proposed method. The classification and change detection results show that the proposed method can reduce the cumulative error with an average classification accuracy of 94.12% and a total detection error of 27.03%, and effectively resolve the “salt and pepper” problem. The proposed method was used to monitor the reclamation status of Liaohe estuary wetland via 10 time series remote sensing images from 1987 to 2014.