Determination of steel quality based on discriminating textural feature selection

2011 ◽  
Vol 66 (23) ◽  
pp. 6264-6271 ◽  
Author(s):  
Daeyoun Kim ◽  
J. Jay Liu ◽  
Chonghun Han
Keyword(s):  
Feature Selection ◽  
Textural Feature ◽  
Steel Quality

A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

Textural feature selection for enhanced detection of stationary humans in through-the-wall radar imagery

2014 ◽  
Author(s):  
A. Chaddad ◽  
F. Ahmad ◽  
M. G. Amin ◽  
P. Sevigny ◽  
D. DiFilippo
Keyword(s):  
Feature Selection ◽  
Textural Feature ◽  
Radar Imagery ◽  
Selection For

scholarly journals Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Rachael Njeri Ndung'u ◽  
Gabriel Ndung’u Kamau ◽  
Geoffrey Wambugu Mariga
Keyword(s):  
Feature Selection ◽  
Recommender Systems ◽  
User Behavior ◽  
Processing Method ◽  
Selection Methods ◽  
Model Accuracy ◽  
Response Variable ◽  
Conventional Systems ◽  
Selection Of

Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines.  In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset.  The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences.

scholarly journals An Optimization-Based Framework for Feature Selection and Parameters Determination of SVMs

2015 ◽  
Vol 7 (5) ◽  
pp. 1-9
Author(s):  
Seyyid Ahmed Medjahed ◽  
◽  
Mohammed Ouali ◽  
Tamazouzt Ait Saadi ◽  
Abdelkader Benyettou
Keyword(s):  
Feature Selection

scholarly journals Determining Threshold Value on Information Gain Feature Selection to Increase Speed and Prediction Accuracy of Random Forest

2020 ◽  
Author(s):  
Maria Irmina Prasetiyowati ◽  
Nur Ulfa Maulidevi ◽  
Kridanto Surendro
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Information Gain ◽  
Threshold Value ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Random Forest Classification ◽  
Average Value ◽  
Time Required

Abstract Feature selection is a preprocessing technique aims to remove the unnecessary features and speed up the algorithm's work process. One of the feature selection techniques is by calculating the information gain value of each feature in a dataset. From the information gain value obtained, then the determined threshold value will be used to make feature selection. Generally, the threshold value is used freely, or using a value of 0.05. This study proposed the determination of the threshold value using the standard deviation of the information gain value generated by each feature in the dataset. The determination of this threshold value was tested on ten original datasets and datasets that had been transformed by FFT and IFFT, then classified using Random Forest. The results of the average value of accuracy and the average time required from the Random Forest classification using the proposed threshold value are better compared to the results of feature selection with a threshold value of 0.05 and the Correlation-Base Feature Selection algorithm. Likewise, the result of the average accuracy value of the proposed threshold using a transformed dataset in terms are better than the threshold value of 0.05 and the Correlation-Base Feature Selection algorithm. However, the calculation results for the average time required are higher (slower).

Use of Orange Data Mining Toolbox for Data Analysis in Clinical Decision Making

2018 ◽  
pp. 143-167
Author(s):  
Musa Peker ◽  
Osman Özkaraca ◽  
Ali Şaşar
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Blood Sugar ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
Proposed Model ◽  
Treatment Of Diabetes

Diabetes is a life-long illness which occurs as a result of lack of insulin hormone or ineffectiveness of insulin hormone. Blood sugar, fructosamine, and hemoglobin A1c (HbA1c) values are widely used for diagnosis of this disease. Although the role of insulin in diagnosing diabetes is great, the HbA1c value is more accurate. This is because HbA1c value gives information about the past two or three months of blood sugar in the treatment of diabetes. This study aims to estimate the HbA1c value with high accuracy. Follow-up data of diabetic patients were used as data. The Orange data mining software is used because it is easy to use in the modeling phase and contains many methods. In this context, the chapter aims to develop an effective prediction model by using a large number of feature selection and classification methods. The results show that the proposed model successfully predicts the HbA1c parameter. In addition, determination of the parameters that are effective in the diagnosis of diabetes has been carried out with the feature selection methods.

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