Large Margin Feature Weighting Method via Linear Programming

2009 ◽  
Vol 21 (10) ◽  
pp. 1475-1488 ◽  
Author(s):  
Bo Chen ◽  
Hongwei Liu ◽  
Jing Chai ◽  
Zheng Bao
Keyword(s):  
Linear Programming ◽  
Feature Weighting ◽  
Weighting Method ◽  
Large Margin

Designing bag-level multiple-instance feature-weighting algorithms based on the large margin principle

2016 ◽  
Vol 367-368 ◽  
pp. 783-808 ◽  
Author(s):  
Jing Chai ◽  
Zehua Chen ◽  
Hongtao Chen ◽  
Xinghao Ding
Keyword(s):  
Feature Weighting ◽  
Large Margin

Emotion evolutions of sub-topics about popular events on microblogs

2017 ◽  
Vol 35 (4) ◽  
pp. 770-782 ◽  
Author(s):  
Qingqing Zhou ◽  
Chengzhi Zhang
Keyword(s):  
Design Methodology ◽  
Topic Model ◽  
Feature Weighting ◽  
User Generated Content ◽  
Emotion Classification ◽  
Weighting Method ◽  
Topic Extraction ◽  
Content Type ◽  
Internet Users ◽  
Large Numbers

Purpose The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic. Design/methodology/approach This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time. Findings Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method. Originality/value To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.

Logistic local hyperplane-Relief: A feature weighting method for classification

2019 ◽  
Vol 181 ◽  
pp. 104741
Author(s):  
Li Zhang ◽  
Xiaojuan Huang ◽  
Weida Zhou
Keyword(s):  
Feature Weighting ◽  
Weighting Method ◽  
Local Hyperplane

Profile Recognition and Mensuration in Machine Vision

1997 ◽  
Vol 119 (3) ◽  
pp. 417-424 ◽  
Author(s):  
S. M. Pandit ◽  
R. Guo
Keyword(s):  
Machine Vision ◽  
Feature Weighting ◽  
Shop Floor ◽  
Sampled Data ◽  
Noisy Environment ◽  
Object Boundary ◽  
Weighting Method ◽  
Characteristic Roots ◽  
Ar Models

This paper presents a systematic profile recognition and mensuration approach in machine vision. It can be utilized to recognize and measure the profiles of industrial parts in an automated manufacturing process by machine vision systems. A new method of profile representation by sampling the data from the object boundary in a digital image is presented. Autoregressive (AR) models are used to code the sampled data of the profiles into AR coefficients for profile recognition. Characterization of the profiles is accomplished by the Data Dependent Systems (DDS) methodology. The AR coefficients and characteristic roots help construct the AR and DDS descriptors to characterize the signatures of the profiles. The frequency domain information about the profiles can be extracted by DDS analysis. The measurement of the profile variation is obtained from the DDS results using optical mensuration method. Neural network and feature weighting method are utilized as reasoning machines for recognition. The illustrative examples in which the profile sampled data are corrupted by noise show that the profile recognition and mensuration approach is very effective and robust in a typical noisy environment on the shop floor.

scholarly journals A  linear programming technique to solve fuzzy multiple criteria decision making problems with an application

2020 ◽  
Author(s):  
S.A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Linear Models ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Radius Of Gyration ◽  
Programming Technique ◽  
Weighting Method ◽  
Practical Applications ◽  
Simple Additive Weighting

Generally, in real world multiple criteria decision making (MCDM) problems, we concern with inaccurate data. This paper transforms a fuzzy multiple criteria decision making (FMCDM) problem into three linear programming models based on simple additive weighting method (SAW). The new linear models calculate fuzzy performance scores for each alternative. To rank the alternatives, the numerical value of the area between the Radius of Gyration (ROG) and original points of the given fuzzy numbers is used. Finally, we illustrate the practical applications of the proposed method in selection an industrial zone for construct dairy products factory.

scholarly journals An Efficient Feature Weighting Method for Support Vector Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Minghua Xie ◽  
Lili Xie ◽  
Peidong Zhu
Keyword(s):  
Support Vector Regression ◽  
Pso Algorithm ◽  
Feature Weighting ◽  
Support Vector ◽  
Weighting Method ◽  
Feature Weights ◽  
Optimal Weights ◽  
Proposed Model ◽  
Synthetic Datasets ◽  
Maximal Information Coefficient

Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time.

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