scholarly journals PREDICTION OF SYMPTOMS PROGRESSION FOR THE PATIENTS WITH KNEE OSTEOARTHRITIS BASED ON THE QUANTITATIVE STRUCTURAL FEATURES: DATA FROM THE FNIH OA BIOMARKERS CONSORTIUM

2021 ◽  
pp. 2140010
Author(s):  
YI XIAO ◽  
FENG XIAO ◽  
HAIBO XU
Keyword(s):  
Feature Selection ◽  
Knee Joint ◽  
Structural Feature ◽  
Cartilage Repair ◽  
Feature Selection Method ◽  
Structural Features ◽  
Selection Method ◽  
Knee Joints ◽  
Joint Symptoms ◽  
Test Sets

Cartilage repair can greatly alleviate the symptoms of the patients with knee osteoarthritis (KOA). However, some imaging results suggest that the patients with obvious cartilage repair may receive insignificant or even no improvement in their symptoms. This study aims to explore the possible reasons based on the structural feature of the knee joint and construct the models used to predict the progression of knee joint symptoms. 551 subjects from Osteoarthritis Biomarkers Consortium FNIH Project in the Osteoarthritis Initiative (OAI) were included and divided into training and test sets. A total of 153 structural features from five quantitative structural feature sets were included to access the structural characteristics of the knee joints. The Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index was used to evaluate the symptoms of the knee joints. A three-step feature selection method were used to screen the structural features. Finally, Naive Bayes (NB), logistic regression (LR), [Formula: see text]-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF) models were constructed based on the selected features, and then compared using the receiver operating characteristic (ROC) curve. The distribution in the demographics and WOMAC symptoms scores of the participants was consistent in the training and test sets. Two demographic features and several structural features were selected using the three-step feature selection method. Among the constructed models, the models used for the progression prediction of pain, stiffness and total scores were better than that of physical function. The performance of RF model was the best while SVM model was the second best, and the performance of the remaining three models in predicting the progression of knee symptoms is indistinguishable. Structural feature-based models for the prediction of knee joint symptoms’ progression were constructed and compared. The constructed model showed good feasibility and accuracy, and may assist clinicians to predict the occurrence or progression of the knee joints symptoms in the evaluation and prognosis of cartilage repair.

scholarly journals Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yaoxin Wang ◽  
Yingjie Xu ◽  
Zhenyu Yang ◽  
Xiaoqing Liu ◽  
Qi Dai
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Feature Selection Method ◽  
Structural Features ◽  
Selection Method ◽  
Prediction Methods ◽  
Information Redundancy ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class

Many combinations of protein features are used to improve protein structural class prediction, but the information redundancy is often ignored. In order to select the important features with strong classification ability, we proposed a recursive feature selection with random forest to improve protein structural class prediction. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed feature selection method effectively improves the efficiency of protein structural class prediction. Only less than 5% features are used, but the prediction accuracy is improved by 4.6-13.3%. We further compared different protein features and found that the predicted secondary structural features achieve the best performance. This understanding can be used to design more powerful prediction methods for the protein structural class.

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