A Methodology for Optimizing the Cost Matrix in Cost Sensitive Learning Models applied to Prediction of Molecular Functions in Embryophyta Plants

2013 ◽  
Keyword(s):  
Learning Models ◽  
Cost Matrix ◽  
Cost Sensitive Learning ◽  
Molecular Functions ◽  
The Cost

On Some Properties of Semi-rings

2018 ◽  
pp. 35-50
Author(s):  
A. I. Belousov
Keyword(s):  
Linear Equations ◽  
Oriented Graph ◽  
Theoretical Computer Science ◽  
Alternative Methods ◽  
Main Role ◽  
Cost Matrix ◽  
Sequential Elimination ◽  
The Matrix ◽  
Systems Of Linear Equations ◽  
The Cost

The main objective of this paper is to prove a theorem according to which a method of successive elimination of unknowns in the solution of systems of linear equations in the semi-rings with iteration gives the really smallest solution of the system. The proof is based on the graph interpretation of the system and establishes a relationship between the method of sequential elimination of unknowns and the method for calculating a cost matrix of a labeled oriented graph using the method of sequential calculation of cost matrices following the paths of increasing ranks. Along with that, and in terms of preparing for the proof of the main theorem, we consider the following important properties of the closed semi-rings and semi-rings with iteration.We prove the properties of an infinite sum (a supremum of the sequence in natural ordering of an idempotent semi-ring). In particular, the proof of the continuity of the addition operation is much simpler than in the known issues, which is the basis for the well-known algorithm for solving a linear equation in a semi-ring with iteration.Next, we prove a theorem on the closeness of semi-rings with iteration with respect to solutions of the systems of linear equations. We also give a detailed proof of the theorem of the cost matrix of an oriented graph labeled above a semi-ring as an iteration of the matrix of arc labels.The concept of an automaton over a semi-ring is introduced, which, unlike the usual labeled oriented graph, has a distinguished "final" vertex with a zero out-degree.All of the foregoing provides a basis for the proof of the main theorem, in which the concept of an automaton over a semi-ring plays the main role.The article's results are scientifically and methodologically valuable. The proposed proof of the main theorem allows us to relate two alternative methods for calculating the cost matrix of a labeled oriented graph, and the proposed proofs of already known statements can be useful in presenting the elements of the theory of semi-rings that plays an important role in mathematical studies of students majoring in software technologies and theoretical computer science.

Searching Best Strategies Algorithm for the No Balance Assignment Problem

2011 ◽  
Vol 50-51 ◽  
pp. 386-390
Author(s):  
Mao Yan Fang ◽  
Min Le Wang ◽  
Yi Ming Bi
Keyword(s):  
Assignment Problem ◽  
Cost Matrix ◽  
Classical Algorithm ◽  
The Cost

The No Balance Assignment Problem (NBAP) is mainly resolved by changing it into Balance Assignment Problem (BAP) and using classical algorithm to deal with it now. This paper proposed Searching Best strategies Algorithm (SBSA) to resolve this problem, and it needn’t to change NBAP into BAP. SBSA resolves NBAP based on searching the best answer of the cost matrix. This algorithm’s theory is simple，and it is easy to operate. The result of the research indicate that the algorithm not only can deal with NBAP, but also can deal with BAP and other problems such as translation problem.

scholarly journals A Multilayer Path Planning Method for High Voltage Distribution Network Based on the Floyd-Warshall Algorithm

2021 ◽  
Vol 2121 (1) ◽  
pp. 012006
Author(s):  
Zhaolong Quan ◽  
Jie Xing ◽  
Ruilin Cao
Keyword(s):  
Path Planning ◽  
Life Cycle Cost ◽  
Cost Model ◽  
Distribution Networks ◽  
Load Flow ◽  
Planning Method ◽  
Cost Matrix ◽  
Planning Scheme ◽  
The Cost ◽  
The City

Abstract With the development of the city, a huge number of distribution networks are waiting for planning. A reasonable planning scheme can meet the power demand and reduce the investment cost. In this paper, a life cycle cost model including the investments of substation and wiring is established with the constraints about load flow calculation and maxi-mum load of wiring. Additionally, a multilayer planning method based on the Floyd-Warshall algorithm has been proposed to solve the model. The area of the city containing substations is divided based on the position of load through the hybrid clusters algorithm in the method. Then, using the divided result of power supply area, the cost matrix for the multilayer path planning method can be constructed through the principle of the method. Lastly, with the cost matrix, the planning scheme in each area will be provided by the Floyd-Warshall algorithm. The result on the actual planning area between the two algorithms shows the total cost of the investment is decreased through using the planning method in this paper.

Cost-Sensitive Learning in Medicine

2011 ◽  
pp. 57-75
Author(s):  
Alberto Freitas ◽  
Pavel Brazdil ◽  
Altamiro Costa-Pereira
Keyword(s):  
Diagnostic Tests ◽  
False Negative ◽  
Cost Sensitive Learning ◽  
Safety Issues ◽  
Misclassification Costs ◽  
Misclassification Errors ◽  
Cost Sensitive Classification ◽  
Human Costs ◽  
The Cost ◽  
Specific Diagnostic Test

This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). In fact, as in other professional areas, both diagnostic tests and its associated misclassification errors can have significant financial or human costs, including the use of unnecessary resource and patient safety issues. This chapter presents some concepts related to cost-sensitive learning and cost-sensitive classification and its application to medicine. Different types of costs are also present, with an emphasis on diagnostic tests and misclassification costs. In addition, an overview of research in the area of cost-sensitive learning is given, including current methodological approaches. Finally, current methods for the cost-sensitive evaluation of classifiers are discussed.

scholarly journals Similarity-Based Clustering for Enhancing Image Classification Architectures

2020 ◽  
Author(s):  
Dishant Parikh ◽  
Shambhavi Aggarwal
Keyword(s):  
Computational Cost ◽  
Image Similarity ◽  
Learning Models ◽  
Additional Information ◽  
Convolutional Networks ◽  
Model Size ◽  
Computer Vision Applications ◽  
The Cost ◽  
Flow Of Information ◽  
Wide Assortment

Convolutional networks are at the center of best in class computer vision applications for a wide assortment of undertakings. Since 2014, profound amount of work began to make better convolutional architectures, yielding generous additions in different benchmarks. Albeit expanded model size and computational cost will, in general, mean prompt quality increases for most undertakings but, the architectures now need to have some additional information to increase the performance. We show empirical evidence that with the amalgamation of content-based image similarity and deep learning models, we can provide the flow of information which can be used in making clustered learning possible. We show how parallel training of sub-dataset clusters not only reduces the cost of computation but also increases the benchmark accuracies by 5-11 percent.

An Efficient Algorithm for Improved Shape Context Feature's Cost Matrix Computation

2012 ◽  
Vol 532-533 ◽  
pp. 1631-1635
Author(s):  
Shan Shan Li ◽  
Ying Hai Zhao ◽  
Jiang An Wang
Keyword(s):  
Efficient Method ◽  
Fourier Transformation ◽  
Time Complexity ◽  
Visual Feature ◽  
Shape Context ◽  
Rotation Invariant ◽  
Matrix Computation ◽  
Cost Matrix ◽  
Feature Based ◽  
The Cost

Shape context is not rotation invariant as a local visual feature. To solve this problem, 2-D and 1-D Fourier Transformation has been performed on the feature. Based on the property of Fourier Transformation, a fast and efficient method is presented in the cost matrix computation of these improved shape context feature. The analysis shows the time complexity is much lower and the experiments show effective and efficiency of this new algorithm.

Optimizing the cost matrix for approximate string matching using genetic algorithms

1998 ◽  
Vol 31 (4) ◽  
pp. 431-440 ◽  
Author(s):  
Marc Parizeau ◽  
Nadia Ghazzali ◽  
Jean-François Hébert
Keyword(s):  
Genetic Algorithms ◽  
String Matching ◽  
Approximate String Matching ◽  
Cost Matrix ◽  
The Cost

scholarly journals SURROGATE MODEL OPTIMIZATION OF A ‘MICRO CORE’ PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS

2021 ◽  
Vol 247 ◽  
pp. 12003
Author(s):  
Andy Whyte ◽  
Geoff Parks
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
Fuel Assembly ◽  
Surrogate Model ◽  
Regression Models ◽  
Model Optimization ◽  
Learning Models ◽  
Training Set ◽  
Surrogate Model Optimization ◽  
The Cost

This paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models.

scholarly journals Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongfu Shao ◽  
Jue Wu ◽  
Hongping Ou ◽  
Min Pei ◽  
Li Liu ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Ultrasound Imaging ◽  
Ultrasound Images ◽  
Cost Matrix ◽  
Imaging Algorithm ◽  
The Cost ◽  
Spatial Frequency Domain

To improve the interpretability or perception of information in images for human viewers is the main goal of image enhancement. Aiming at the problem that image edges are difficult to determine due to artefacts, plaques, and vascular branches in cardiovascular ultrasound, an edge ultrasound imaging detection algorithm based on spatial-frequency-domain image enhancement is proposed to improve the clarity of ultrasound images. Firstly, this paper uses the space-frequency-domain enhancement algorithm to enhance the image. This algorithm overcomes the problem of low contrast of conventional algorithms. The enhanced data matrix is used as the cost matrix, and then, the heuristic image search method is used to search the image of the cost matrix. The results show that the use of spatial-frequency-domain image ultrasound imaging algorithm can improve the contrast and sharpness of ultrasound images of cardiovascular disease, which can make the middle edge of the image clearer, the detection accuracy rate is increased to 92.76%, and the ultrasound of cardiovascular disease is improved. The edge of the image gets accuracy. The paper confirms that the ultrasound imaging algorithm based on spatial-frequency-domain image enhancement is worthy of application in clinical ultrasound image processing. The performance of the proposed technique is 32.54%, 75.30%, 21.19%, 21.26%, and 11.10% better than the existing technique in terms of edge energy, detail energy, sharpness, contrast, and information entropy, respectively.

scholarly journals Prediction of Employee Attrition Using Machine Learning and Ensemble Methods

2021 ◽  
Vol 11 (2) ◽  
pp. 110-114
Author(s):  
Aseel Qutub ◽  
◽  
Asmaa Al-Mehmadi ◽  
Munirah Al-Hssan ◽  
Ruyan Aljohani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Professional Training ◽  
Ensemble Methods ◽  
Gradient Boosting ◽  
Learning Models ◽  
Retention Strategies ◽  
Employee Attrition ◽  
The Cost ◽  
Machine Learning Models

Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, Adaboost Model, and Gradient Boosting Classifier models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.

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