Decision Fusion of Multiple Classifiers for Coronary Plaque Characterization from IVUS Images

2014 ◽  
Vol 23 (03) ◽  
pp. 1460005 ◽  
Author(s):  
V. G. Giannoglou ◽  
J. B. Theocharis
Keyword(s):  
Lipid A ◽  
Tissue Characterization ◽  
Vascular Tissue ◽  
Feature Space ◽  
Coronary Plaque ◽  
Decision Fusion ◽  
Plaque Component ◽  
Multiple Classifiers ◽  
Combination Approach ◽  
The Individual

Vascular tissue characterization is of great importance concerning the possibility of an Acute Cardiac Syndrome (ACS). Gray-scale intravascular ultrasound (IVUS) is a powerful tomographic modality providing a thorough visualization of coronary arteries. Among the existing methods, virtual histology (VH) is the most popular and clinically available technique for plaque component analysis, it suffers however from a poor longitudinal resolution. In order to surmount this demerit, a new image-based methodology for plaque assessment is suggested here that differentiates tissue components into four classes: calcium, necrotic core, fibrous and fibro-lipid. A rich set of five textural feature families are extracted from IVUS images, computed at different scales. The main contribution of this paper is that tissue classification is accomplished using the principles of multiple classifiers combination approach. At the first stage, an ensemble of base SVM classifiers is constructed from each feature family, separately. The fuzzy outputs of the individual classifiers are then aggregated to provide the final fused results. We investigate four efficient decision fusion schemes of the literature and the SVM fuser. Extensive experimentation is carried out to highlight the merits of the suggested schemes against single SVM classifiers that use reduced feature subsets obtained after feature selection or the entire feature space. The analysis demonstrates that the decision fusion techniques offer improved classification accuracies, compared to single SVM classifiers and existing methods in IVUS imaging. In addition, the method provides accurate assessments of plaque composition in IVUS images.

Advanced Security Incident Analysis with Sensor Correlation

2012 ◽  
pp. 302-319
Author(s):  
Ciza Thomas ◽  
N. Balakrishnan
Keyword(s):  
Intrusion Detection ◽  
Poor Performance ◽  
Feature Space ◽  
Decision Fusion ◽  
Intrusion Detection Systems ◽  
Security Incident ◽  
Detection Systems ◽  
Improved Performance ◽  
Using Data ◽  
The Individual

This chapter explores the general problem of the poorly detected attacks with Intrusion Detection Systems. The poorly detected attacks reveal the fact that they are characterized by features that do not discriminate them much. The poor performance of the detectors has been improved by discriminative training of anomaly detectors and incorporating additional rules into the misuse detector. This chapter proposes a new approach of machine learning method where corresponding learning problem is characterized by a number of features. This chapter discusses the improved performance of multiple Intrusion Detection Systems using Data-dependent Decision fusion. The Data-dependent Decision fusion approach gathers an in-depth understanding about the input traffic and also the behavior of the individual Intrusion Detection Systems by means of a neural network learner unit. This information is used to fine-tune the fusion unit since the fusion depends on the input feature vector. Thus fusion implements a function that is local to each region in the feature space. It is well-known that the effectiveness of sensor fusion improves when the individual IDSs are uncorrelated. The training methodology adopted in this work takes note of this fact. For illustrative purposes, the DARPA 1999 data set as has been used. The Data-dependent Decision fusion shows a significantly better performance with respect to the performance of individual Intrusion Detection Systems.

scholarly journals A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction

2021 ◽  
Author(s):  
Ferdinand Bollwein ◽  
Stephan Westphal
Keyword(s):  
Decision Tree ◽  
Feature Space ◽  
Classification Problems ◽  
Decision Tree Induction ◽  
Single Attribute ◽  
Global Optimal ◽  
The Individual ◽  
Tree Building ◽  
Very High ◽  
Multiclass Classification Problems

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.

scholarly journals Physical Activity and Redox Balance in the Elderly: Signal Transduction Mechanisms

2021 ◽  
Vol 11 (5) ◽  
pp. 2228
Author(s):  
Daniela Galli ◽  
Cecilia Carubbi ◽  
Elena Masselli ◽  
Mauro Vaccarezza ◽  
Valentina Presta ◽  
...  
Keyword(s):  
Physical Activity ◽  
Molecular Mechanisms ◽  
Vascular Tissue ◽  
The Elderly ◽  
Redox Balance ◽  
Antioxidant Systems ◽  
Targeted Prevention ◽  
Individual Subject ◽  
Aged People ◽  
The Individual

Reactive Oxygen Species (ROS) are molecules naturally produced by cells. If their levels are too high, the cellular antioxidant machinery intervenes to bring back their quantity to physiological conditions. Since aging often induces malfunctioning in this machinery, ROS are considered an effective cause of age-associated diseases. Exercise stimulates ROS production on one side, and the antioxidant systems on the other side. The effects of exercise on oxidative stress markers have been shown in blood, vascular tissue, brain, cardiac and skeletal muscle, both in young and aged people. However, the intensity and volume of exercise and the individual subject characteristics are important to envisage future strategies to adequately personalize the balance of the oxidant/antioxidant environment. Here, we reviewed the literature that deals with the effects of physical activity on redox balance in young and aged people, with insights into the molecular mechanisms involved. Although many molecular pathways are involved, we are still far from a comprehensive view of the mechanisms that stand behind the effects of physical activity during aging. Although we believe that future precision medicine will be able to transform exercise administration from wellness to targeted prevention, as yet we admit that the topic is still in its infancy.

scholarly journals Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation

2020 ◽  
Vol 12 (20) ◽  
pp. 3342
Author(s):  
Haoyang Yu ◽  
Xiao Zhang ◽  
Meiping Song ◽  
Jiaochan Hu ◽  
Qiandong Guo ◽  
...  
Keyword(s):  
Image Classification ◽  
Satellite Data ◽  
Hyperspectral Image ◽  
Training Sample ◽  
Decision Fusion ◽  
Spatial Constraint ◽  
Hyperspectral Image Classification ◽  
Object Based ◽  
Sparse Coefficient ◽  
The Individual

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample in representing the testing pixel. However, the spatial variants of the original residual error-driven frameworks often suffer the obstacles to optimization due to the strong constraints. In this paper, based on the object-based image classification (OBIC) framework, we firstly propose a spectral–spatial classification method, called superpixel-level constraint representation (SPCR). Firstly, it uses the PD in respect to the sparse coefficient from CR model. Then, transforming the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) is further proposed through the decision fusion process of SPCR at different scales. The SPCR method is firstly performed at each scale, and the final category of the testing pixel is determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the two proposed methods.

A New Approach for Improving Coronary Plaque Component Analysis Based on Intravascular Ultrasound Images

2010 ◽  
Vol 36 (8) ◽  
pp. 1245-1258 ◽  
Author(s):  
Arash Taki ◽  
Holger Hetterich ◽  
Alireza Roodaki ◽  
S.K. Setarehdan ◽  
Gozde Unal ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Coronary Plaque ◽  
Component Analysis ◽  
Ultrasound Images ◽  
New Approach ◽  
Plaque Component

Shift-Invariance of Pattern Recognition in the Visual Field?

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 78-78 ◽  
Author(s):  
M Jüttner ◽  
I Rentschler ◽  
A Unzicker
Keyword(s):  
Visual Field ◽  
Feature Space ◽  
Classification Performance ◽  
Classification Model ◽  
Perceptual Dimension ◽  
Internal Representations ◽  
Visual Hemifield ◽  
Shift Invariance ◽  
Hemispheric Specialisation ◽  
The Individual

The classification behaviour of human observers with respect to compound Gabor signals was tested at foveal and extrafoveal retinal positions. Classification performance was analysed in terms of a probabilistic classification model recently proposed by Rentschler, Jüttner, and Caelli (1994 Vision Research34 669 – 687). The analysis allows inferences about structure and dimensionality of the individual internal representations underlying the classification task and their temporal evolution during the learning process. With this technique it was found that the internal representations of direct and eccentric viewing are intrinsically incommensurable in the sense that extrafoveal pattern representations are characterised by a lower perceptual dimension in feature space relative to the corresponding physical input signals, whereas foveal representations are not (Jüttner and Rentschler, 1996 Vision Research in press). We then addressed the question to what extent observers are capable of generalising class concepts that have been acquired at one particular retinal location to other retinal sites. We found partial generalisation with respect to spatial translation across the visual field. Moreover, there is, in the case of extrafoveal learning, a distinct asymmetry in performance with respect to the visual hemifield in which the signals were originally learned. The latter finding can be related to functional hemispheric specialisation in pattern learning and recognition.

scholarly journals Combined classifier based on feature space partitioning

2012 ◽  
Vol 22 (4) ◽  
pp. 855-866 ◽  
Author(s):  
Michał Woźniak ◽  
Bartosz Krawczyk
Keyword(s):  
Computer Experiments ◽  
Feature Space ◽  
Majority Voting ◽  
Space Partitioning ◽  
Voting Rule ◽  
Benchmark Datasets ◽  
Proposed Modification ◽  
The Individual ◽  
Combined Classifier ◽  
Feature Space Partitioning

This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.

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