scholarly journals Fuzzy ARTMAP Ensemble Based Decision Making and Application

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Min Jin ◽  
Zengbing Xu ◽  
Ren Li ◽  
Dan Wu
Keyword(s):  
Decision Making ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Fuzzy Artmap ◽  
Final Decision ◽  
Ensemble Approach ◽  
Training Samples ◽  
Sample Presentation

Because the performance of single FAM is affected by the sequence of sample presentation for the offline mode of training, a fuzzy ARTMAP (FAM) ensemble approach based on the improved Bayesian belief method is supposed to improve the classification accuracy. The training samples are input into a committee of FAMs in different sequence, the output from these FAMs is combined, and the final decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs’ ensemble can classify the different category reliably and has a better classification performance compared with single FAM.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Effects of Class Purity of Training Patch on Classification Performance of Crop Classification with Convolutional Neural Network

2020 ◽  
Vol 10 (11) ◽  
pp. 3773
Author(s):  
Soyeon Park ◽  
No-Wook Park
Keyword(s):  
Classification Accuracy ◽  
Supervised Classification ◽  
Patch Size ◽  
Classification Performance ◽  
Spatial Homogeneity ◽  
High Class ◽  
Large Size ◽  
Training Samples ◽  
Compositional Homogeneity ◽  
Crop Classification

As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area.

scholarly journals Hybrid Dense Network with Dual Attention for Hyperspectral Image Classification

2021 ◽  
Vol 13 (23) ◽  
pp. 4921
Author(s):  
Jinling Zhao ◽  
Lei Hu ◽  
Yingying Dong ◽  
Linsheng Huang
Keyword(s):  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Network Models ◽  
Classification Performance ◽  
Dense Network ◽  
Feature Maps ◽  
Neural Network Models ◽  
Practical Applications ◽  
Training Samples ◽  
3D Cnn

Hyperspectral images (HSIs) have been widely used in many fields of application, but it is still extremely challenging to obtain higher classification accuracy, especially when facing a smaller number of training samples in practical applications. It is very time-consuming and laborious to acquire enough labeled samples. Consequently, an efficient hybrid dense network was proposed based on a dual-attention mechanism, due to limited training samples and unsatisfactory classification accuracy. The stacked autoencoder was first used to reduce the dimensions of HSIs. A hybrid dense network framework with two feature-extraction branches was then established in order to extract abundant spectral–spatial features from HSIs, based on the 3D and 2D convolutional neural network models. In addition, spatial attention and channel attention were jointly introduced in order to achieve selective learning of features derived from HSIs. The feature maps were further refined, and more important features could be retained. To improve computational efficiency and prevent the overfitting, the batch normalization layer and the dropout layer were adopted. The Indian Pines, Pavia University, and Salinas datasets were selected to evaluate the classification performance; 5%, 1%, and 1% of classes were randomly selected as training samples, respectively. In comparison with the REF-SVM, 3D-CNN, HybridSN, SSRN, and R-HybridSN, the overall accuracy of our proposed method could still reach 96.80%, 98.28%, and 98.85%, respectively. Our results show that this method can achieve a satisfactory classification performance even in the case of fewer training samples.

The Elimination of Irrelevant Information in a Multidimensional Stimulus Classification Task

1978 ◽  
Vol 20 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Louis F. Cicchinelli ◽  
Alma E. Lantz
Keyword(s):  
Decision Making ◽  
Stimulus Control ◽  
Classification Accuracy ◽  
Significant Positive Correlation ◽  
Stimulus Display ◽  
Irrelevant Information ◽  
Classification Performance ◽  
Stimulus Information ◽  
Amount Of Information ◽  
Stimulus Classification

Two experiments were conducted to investigate the effect of providing stimulus control on classification performance. In the first study, subjects were given control of various stimulus dimensions during a series of seven sessions. There was no difference in classification accuracy between the group having stimulus control and the group having no stimulus control. A significant positive correlation was obtained between the amount of information eliminated and classification accuracy. This finding suggested that subjects were able to filter some stimulus information overtly while increasing classification accuracy. The second study was designed to determine if the elimination of information produced a more “efficient” stimulus display. Subjects viewed either a “filtered” display created by subjects in Experiment 1, or the original “nonfiltered” display. Results showed that subjects viewing the “filtered” displays were significantly more accurate in classifying stimuli during the first session. Further, there was a significant correlation between the accuracy of subjects producing the “filtered” display and the subjects utilizing it. The results were discussed as a procedure for isolating the necessary information parameters for decision making.

How multidisciplinary are multidisciplinary case reviews in cancer care? Feasibility analysis of a theory-driven team decision-making fidelity framework

2019 ◽  
Author(s):  
Tayana Soukup ◽  
Ged Murtagh ◽  
Ben W Lamb ◽  
James Green ◽  
Nick Sevdalis
Keyword(s):  
Decision Making ◽  
Cancer Care ◽  
Recent Decade ◽  
Individual Case ◽  
Multidisciplinary Teams ◽  
Final Decision ◽  
Political Climate ◽  
Implementation Framework ◽  
Feasibility Evaluation ◽  
The Uk

Background Multidisciplinary teams (MDTs) are a standard cancer care policy in many countries worldwide. Despite an increase in research in a recent decade on MDTs and their care planning meetings, the implementation of MDT-driven decision-making (fidelity) remains unstudied. We report a feasibility evaluation of a novel method for assessing cancer MDT decision-making fidelity. We used an observational protocol to assess (1) the degree to which MDTs adhere to the stages of group decision-making as per the ‘Orientation-Discussion-Decision-Implementation’ framework, and (2) the degree of multidisciplinarity underpinning individual case reviews in the meetings. MethodsThis is a prospective observational study. Breast, colorectal and gynaecological cancer MDTs in the Greater London and Derbyshire (United Kingdom) areas were video recorded over 12-weekly meetings encompassing 822 case reviews. Data were coded and analysed using frequency counts.Results Eight interaction formats during case reviews were identified. case reviews were not always multi-disciplinary: only 8% of overall reviews involved all five clinical disciplines present, and 38% included four of five. The majority of case reviews (i.e. 54%) took place between two (25%) or three (29%) disciplines only. Surgeons (83%) and oncologists (8%) most consistently engaged in all stages of decision-making. While all patients put forward for MDT review were actually reviewed, a small percentage of them (4%) either bypassed the orientation (case presentation) and went straight into discussing the patient, or they did not articulate the final decision to the entire team (8%). Conclusions Assessing fidelity of MDT decision-making at the point of their weekly meetings is feasible. We found that despite being a set policy, case reviews are not entirely MDT-driven. We discuss implications in relation to the current eco-political climate, and the quality and safety of care. Our findings are in line with the current national initiatives in the UK on streamlining MDT meetings, and could help decide how to re-organise them to be most efficient.

scholarly journals What’s in a (Change of) Name? Much—but Not That Much—and Not What Wiebe Claims

2020 ◽  
Vol 32 (2) ◽  
pp. 159-184 ◽  
Author(s):  
Satoko Fujiwara ◽  
Tim Jensen
Keyword(s):  
Decision Making ◽  
Executive Committee ◽  
International Association ◽  
International Committee ◽  
Academic Standards ◽  
General Assembly ◽  
Final Decision ◽  
Points Of View ◽  
The Difference ◽  
Consultative Body

Abstract Donald Wiebe claims that the IAHR leadership (already before an Extended Executive Committee (EEC) meeting in Delphi) had decided to water down the academic standards of the IAHR with a proposal to change its name to “International Association for the Study of Religions.” His criticism, we argue, is based on a series of misunderstandings as regards: 1) the difference between the consultative body (EEC) and the decision-making body (EC), 2) the difference between the preliminary points of view of individuals and final proposals by the EC, 3) personal conversations, 4) the link between the proposal to change the name and the wish to tighten up the academic profile of the IAHR. Moreover, if the final decision-making bodies, the International Committee and the General Assembly, adopt the proposal, the new name as little as the old can make the IAHR more or less scientific. Tightening up the academic, scientific profile of the IAHR takes more than a change of name.

ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

scholarly journals Signal Classification Algorithms over Time Selective Channels

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1714
Author(s):  
Mohamed Marey ◽  
Hala Mostafa
Keyword(s):  
Block Code ◽  
Classification Problem ◽  
Classification Performance ◽  
Signal Classification ◽  
Final Decision ◽  
Time Block ◽  
Channel Response ◽  
Over Time ◽  
Parallel Fashion

In this work, we propose a general framework to design a signal classification algorithm over time selective channels for wireless communications applications. We derive an upper bound on the maximum number of observation samples over which the channel response is an essential invariant. The proposed framework relies on dividing the received signal into blocks, and each of them has a length less than the mentioned bound. Then, these blocks are fed into a number of classifiers in a parallel fashion. A final decision is made through a well-designed combiner and detector. As a case study, we employ the proposed framework on a space-time block-code classification problem by developing two combiners and detectors. Monte Carlo simulations show that the proposed framework is capable of achieving excellent classification performance over time selective channels compared to the conventional algorithms.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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