Connectionist Approach to Improving Highway Vehicle Classification Schemes

2005 ◽  
Vol 1917 (1) ◽  
pp. 182-189
Author(s):  
Valerian Kwigizile ◽  
Renatus N. Mussa ◽  
Majura Selekwa
Keyword(s):  
Neural Networks ◽  
Error Rate ◽  
Classification Problem ◽  
Vehicle Classification ◽  
Classification Error ◽  
Classification Error Rate ◽  
Pattern Recognition Problem ◽  
Vehicle Weight ◽  
Load Spectra ◽  
Set Up

The mechanistic–empirical pavement design methodology being developed under NCHRP Project 1–37A will require accurate classification of vehicles to develop axle load spectra information needed as the design input. Scheme F, used by most states to classify vehicles, can be used to develop the required load spectra. Unfortunately, the scheme is difficult to automate and is prone to errors resulting from imprecise demarcation of class thresholds. In this paper, the classification problem is viewed as a pattern recognition problem in which connectionist techniques such as probabilistic neural networks (PNN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. The PNN was developed, trained, and applied to field data composed of individual vehicles’ axle spacing, number of axles per vehicle, and overall vehicle weight. The PNN reduced the error rate from 9.5% to 6.2% compared with an existing classification algorithm used by the Florida Department of Transportation. The inclusion of overall vehicle weight as a classification variable further reduced the error rate from 6.2% to 3.0%. The promising results from neural networks were used to set up new thresholds that reduce classification error rate.

scholarly journals The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme

2021 ◽  
Vol 11 (17) ◽  
pp. 7883
Author(s):  
Anas Husseis ◽  
Judith Liu-Jimenez ◽  
Raul Sanchez-Reillo
Keyword(s):  
Error Rate ◽  
Attack Detection ◽  
Fingerprint Recognition ◽  
Classification Error ◽  
Final Decision ◽  
Classification Error Rate ◽  
Detection Scheme ◽  
Bona Fide ◽  
The Impact ◽  
Presentation Attack Detection

Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both.

scholarly journals A variant of gravitational classification

2014 ◽  
Vol 51 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Tomasz Górecki ◽  
Maciej Luczak
Keyword(s):  
Gravity Field ◽  
Error Rate ◽  
Simulation Method ◽  
General Idea ◽  
Classification Error ◽  
Data Sets ◽  
Classification Error Rate ◽  
Motion Problem ◽  
The Mean ◽  
Parameter Values

Summary In this article there is proposed a new two-parametrical variant of the gravitational classification method. We use the general idea of objects' behavior in a gravity field. Classification depends on a test object's motion in a gravity field of training points. To solve this motion problem, we use a simulation method. This classifier is compared to the 1NN method, because our method tends towards it for some parameter values. Experimental results on different data sets demonstrate an improvement in efficiency and that this approach outperforms the 1NN method by providing a significant reduction in the mean classification error rate.

Using Classification Error Rate a New Image-Extraction Method of Debris Cloud at Hypervelocity Impact

2015 ◽  
Vol 63 (4) ◽  
pp. 157-165
Author(s):  
Yoshihiro OKI ◽  
Norihiko MATSUMOTO ◽  
Kiyonobu OHTANI ◽  
Sunao HASEGAWA ◽  
Kanjuro MAKIHARA
Keyword(s):  
Error Rate ◽  
Extraction Method ◽  
Hypervelocity Impact ◽  
Classification Error ◽  
Classification Error Rate ◽  
Debris Cloud

An Evaluation of Smoothed Classification Error – Rate Estimators

Technometrics ◽  
1985 ◽  
Vol 27 (2) ◽  
pp. 199-206 ◽  
Author(s):  
Steven M. Snapinn ◽  
James D. Knoke
Keyword(s):  
Error Rate ◽  
Classification Error ◽  
Classification Error Rate

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

2010 ◽  
Vol 49 (03) ◽  
pp. 254-268 ◽  
Author(s):  
C.-S. Yang ◽  
K.-C. Wu ◽  
C.-H. Yang ◽  
L.-Y. Chuang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Microarray Data ◽  
Error Rate ◽  
Gene Expression Analysis ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Error ◽  
Classification Error Rate

Summary Background: Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. Objective: The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Method: In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Results: Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. Conclusion: The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

Flaw Classification of Flexible Copper Clad-Laminated Sheet

2012 ◽  
Vol 579 ◽  
pp. 52-59
Author(s):  
Yih Chih Chiou ◽  
Yu Teng Liang
Keyword(s):  
Error Rate ◽  
Copper Foil ◽  
Classification Error ◽  
Support Vector ◽  
Classification Rate ◽  
Classification Error Rate ◽  
Vector Machines ◽  
Flaw Classification ◽  
Misclassification Rates

This study investigated the classification error rate of eleven flaws commonly occurred in copper foil. The goal was to online identify the type of the flaw being discovered in order to trace the source of the flaw and act correspondingly. The misclassification rates of four popular classifiers were investigated and compared. The results indicated that the best classification rate can be obtained by choosing Support Vector Machines as the classifier and employing all the ten features. The resulting low classification error rate of 4.41% proved the effectiveness of the derived classifier as well as the suitability of the chosen features.

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