CORPS_F: A NEW TOOL FOR FEATURE ASSESSMENT IN IMPRECISELY SUPERVISED OR FUZZY ENVIRONMENTS

1995 ◽  
Vol 03 (04) ◽  
pp. 451-464 ◽  
Author(s):  
BELUR V. DASARATHY
Keyword(s):  
Pattern Recognition ◽  
Computationally Efficient ◽  
Training Samples ◽  
Detailed Evaluation ◽  
Efficient Scheme ◽  
Selection For ◽  
Class Labels ◽  
Overlap Region

This study presents an effective approach to the hitherto little addressed problem of feature assessment and selection for pattern recognition in imprecisely supervised environments. Unlike in classical supervised environments wherein the representative training samples have crisp class labels, here the samples have fuzzy memberships in several of the different pattern classes in the environment. The new methodology reported here is an outgrowth of a recently developed tool CORPS—Class Overlap Region Partitioning Scheme initially designed for operation in supervised environments and extended later for operation in imperfectly supervised environments. The emphasis here has been the development of a computationally efficient scheme capable of evaluating as rapidly as practical a large number of features individually as to their discrimination potential based on which a smaller subset may be selected, if so desired, for more detailed evaluation in different combinations by other tools.

Pattern recognition and feature selection for the discrimination between grades of commercial plastics

2007 ◽  
Vol 87 (1) ◽  
pp. 18-25 ◽  
Author(s):  
Bozena M. Lukasiak ◽  
Simeone Zomer ◽  
Richard G. Brereton ◽  
Rita Faria ◽  
John C. Duncan
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Selection For

scholarly journals Antenna selection for MIMO system based on pattern recognition

2019 ◽  
Vol 5 (1) ◽  
pp. 34-39 ◽  
Author(s):  
Ping Yang ◽  
Jing Zhu ◽  
Yue Xiao ◽  
Zhi Chen
Keyword(s):  
Pattern Recognition ◽  
Antenna Selection ◽  
Mimo System ◽  
Selection For

Gate location optimization scheme for plastic injection molding

e-Polymers ◽  
2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Ming Zhai ◽  
Yeecheong Lam ◽  
Chikit Au
Keyword(s):  
Injection Molding ◽  
Objective Function ◽  
Flow Path ◽  
Computationally Efficient ◽  
Gate Location ◽  
Adjacent Node ◽  
Filling Time ◽  
Efficient Scheme ◽  
Balanced Flow ◽  
Search Routine

AbstractGate location of injection molding is vital to achieve high quality plastic part. The determination of gate location is an important issue in mold design. A computationally efficient scheme based on flow path is proposed to locate the optimum gate for achieving balanced flow. The range of filling time is employed as objective function. Comparisons were made between the flow path search scheme and the existing adjacent node evaluation scheme, and between the objective function of the range of filling time and the existing standard deviation of filling time. The two examples investigated indicated that the search routine based on the concept of flow path is more efficient computationally and the range of filling time is a better objective function to reflect the uniformity of fill.

scholarly journals Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 543 ◽  
Author(s):  
Konrad Furmańczyk ◽  
Wojciech Rejchel
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Logistic Model ◽  
Binary Classification ◽  
Model Misspecification ◽  
High Dimensional ◽  
Classification Models ◽  
Computationally Efficient ◽  
Class Labels ◽  
Penalized Logistic Regression

In this paper, we consider prediction and variable selection in the misspecified binary classification models under the high-dimensional scenario. We focus on two approaches to classification, which are computationally efficient, but lead to model misspecification. The first one is to apply penalized logistic regression to the classification data, which possibly do not follow the logistic model. The second method is even more radical: we just treat class labels of objects as they were numbers and apply penalized linear regression. In this paper, we investigate thoroughly these two approaches and provide conditions, which guarantee that they are successful in prediction and variable selection. Our results hold even if the number of predictors is much larger than the sample size. The paper is completed by the experimental results.

scholarly journals Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jiajia Liu ◽  
Bailin Li ◽  
Ying Xiong ◽  
Biao He ◽  
Li Li
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Identification Problem ◽  
Test Sample ◽  
Computationally Efficient ◽  
Railway Traffic ◽  
Training Samples ◽  
Inspection Systems ◽  
Representation Method ◽  
Defect Recognition

The detection of fastener defects is an important task for ensuring the safety of railway traffic. The earlier automatic inspection systems based on computer vision can detect effectively the completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In this paper, we propose a method for detecting both partly worn and completely missing fasteners, the proposed algorithm exploits the first and second symmetry sample of original testing fastener image and integrates them for improved representation-based fastener recognition. This scheme is simple and computationally efficient. The underlying rationales of the scheme are as follows: First, the new virtual symmetrical images really reflect some possible appearance of the fastener; then the integration of two judgments of the symmetrical sample for fastener recognition can somewhat overcome the misclassification problem. Second, the improved sparse representation method discarding the training samples that are “far” from the test sample and uses a small number of samples that are “near” to the test sample to represent the test sample, so as to perform classification and it is able to reduce the side-effect of the error identification problem of the original fastener image. The experimental results show that the proposed method outperforms state-of-the-art fastener recognition methods.

scholarly journals Theoretical and Experimental Analyses of Tensor-Based Regression and Classification

2016 ◽  
Vol 28 (4) ◽  
pp. 686-715 ◽  
Author(s):  
Kishan Wimalawarne ◽  
Ryota Tomioka ◽  
Masashi Sugiyama
Keyword(s):  
Real Data ◽  
Optimization Methods ◽  
Alternating Direction Method ◽  
Trace Norm ◽  
Method Of Multipliers ◽  
Computationally Efficient ◽  
Learning Methods ◽  
Alternating Direction ◽  
Training Samples ◽  
Tensor Norm

We theoretically and experimentally investigate tensor-based regression and classification. Our focus is regularization with various tensor norms, including the overlapped trace norm, the latent trace norm, and the scaled latent trace norm. We first give dual optimization methods using the alternating direction method of multipliers, which is computationally efficient when the number of training samples is moderate. We then theoretically derive an excess risk bound for each tensor norm and clarify their behavior. Finally, we perform extensive experiments using simulated and real data and demonstrate the superiority of tensor-based learning methods over vector- and matrix-based learning methods.

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