scholarly journals ROC-Boosting: A Feature Selection Method for Health Identification Using Tongue Image

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Cui ◽  
Shizhong Liao ◽  
Hongwu Wang
Keyword(s):  
Feature Selection ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Health Information ◽  
Scientific Evidence ◽  
Feature Selection Method ◽  
Selection Method ◽  
Tongue Diagnosis ◽  
Health Group ◽  
Boosting Method

Objective. To select significant Haar-like features extracted from tongue images for health identification.Materials and Methods. 1,322 tongue cases were included in this study. Health information and tongue images of each case were collected. Cases were classified into the following groups: group containing 148 cases diagnosed as health; group containing 332 cases diagnosed as ill based on health information, even though tongue image is normal; and group containing 842 cases diagnosed as ill. Haar-like features were extracted from tongue images. Then, we proposed a new boosting method in the ROC space for selecting significant features from the features extracted from these images.Results. A total of 27 features were obtained from groups A, B, and C. Seven features were selected from groups A and B, while 25 features were selected from groups A and C.Conclusions. The selected features in this study were mainly obtained from the root, top, and side areas of the tongue. This is consistent with the tongue partitions employed in traditional Chinese medicine. These results provide scientific evidence to TCM tongue diagnosis for health identification.

scholarly journals Research on Hybrid Feature Selection Method Based on Iterative Approximation Markov Blanket

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Canyi Huang ◽  
Keding Li ◽  
Jianqiang Du ◽  
Bin Nie ◽  
Guoliang Xu ◽  
...  
Keyword(s):  
Experimental Data ◽  
Feature Selection ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Subset ◽  
Iterative Approximation ◽  
Markov Blanket ◽  
Basic Experimental Data

The basic experimental data of traditional Chinese medicine are generally obtained by high-performance liquid chromatography and mass spectrometry. The data often show the characteristics of high dimensionality and few samples, and there are many irrelevant features and redundant features in the data, which bring challenges to the in-depth exploration of Chinese medicine material information. A hybrid feature selection method based on iterative approximate Markov blanket (CI_AMB) is proposed in the paper. The method uses the maximum information coefficient to measure the correlation between features and target variables and achieves the purpose of filtering irrelevant features according to the evaluation criteria, firstly. The iterative approximation Markov blanket strategy analyzes the redundancy between features and implements the elimination of redundant features and then selects an effective feature subset finally. Comparative experiments using traditional Chinese medicine material basic experimental data and UCI’s multiple public datasets show that the new method has a better advantage to select a small number of highly explanatory features, compared with Lasso, XGBoost, and the classic approximate Markov blanket method.

scholarly journals Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Canyi Huang ◽  
Jianqiang Du ◽  
Bin Nie ◽  
Riyue Yu ◽  
Wangping Xiong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Subset

The partial least squares method has many advantages in multivariable linear regression, but it does not include the function of feature selection. This method cannot screen for the best feature subset (referred to in this study as the “Gold Standard”) or optimize the model, although contrarily using the L1 norm can achieve the sparse representation of parameters, leading to feature selection. In this study, a feature selection method based on partial least squares is proposed. In the new method, exploiting partial least squares allows extraction of the latent variables required for performing multivariable linear regression, and this method applies the L1 regular term constraint to the sum of the absolute values of the regression coefficients. This technique is then combined with the coordinate descent method to perform multiple iterations to select a better feature subset. Analyzing traditional Chinese medicine data and University of California, Irvine (UCI), datasets with the model, the experimental results show that the feature selection method based on partial least squares exhibits preferable adaptability for traditional Chinese medicine data and UCI datasets.

Improvement of feature selection method in spam filtering

2009 ◽  
Vol 29 (10) ◽  
pp. 2812-2815
Author(s):  
Yang-zhu LU ◽  
Xin-you ZHANG ◽  
Yu QI
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Spam Filtering

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

A lazy feature selection method for multi-label classification

2021 ◽  
Vol 25 (1) ◽  
pp. 21-34
Author(s):  
Rafael B. Pereira ◽  
Alexandre Plastino ◽  
Bianca Zadrozny ◽  
Luiz H.C. Merschmann
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Selection Method ◽  
Video Classification ◽  
Classification Problems ◽  
Class Label ◽  
New Feature ◽  
Feature Selection Techniques ◽  
Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1226
Author(s):  
Saeed Najafi-Zangeneh ◽  
Naser Shams-Gharneh ◽  
Ali Arjomandi-Nezhad ◽  
Sarfaraz Hashemkhani Zolfani
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Standard Deviation ◽  
Analytical Formula ◽  
Feature Selection Method ◽  
Selection Method ◽  
Performance Measure ◽  
Learning Approaches ◽  
Training Costs ◽  
Professional Employees

Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions. Unlike physical systems, human resource problems cannot be described by a scientific-analytical formula. Therefore, machine learning approaches are the best tools for this aim. This paper presents a three-stage (pre-processing, processing, post-processing) framework for attrition prediction. An IBM HR dataset is chosen as the case study. Since there are several features in the dataset, the “max-out” feature selection method is proposed for dimension reduction in the pre-processing stage. This method is implemented for the IBM HR dataset. The coefficient of each feature in the logistic regression model shows the importance of the feature in attrition prediction. The results show improvement in the F1-score performance measure due to the “max-out” feature selection method. Finally, the validity of parameters is checked by training the model for multiple bootstrap datasets. Then, the average and standard deviation of parameters are analyzed to check the confidence value of the model’s parameters and their stability. The small standard deviation of parameters indicates that the model is stable and is more likely to generalize well.

Sign in / Sign up

Export Citation Format

Share Document