scholarly journals Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1255 ◽  
Author(s):  
Malik Yousef ◽  
Burcu Bakir-Gungor ◽  
Amhar Jabeer ◽  
Gokhan Goy ◽  
Rehman Qureshi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Simple Structure ◽  
Selection Process ◽  
Ranking Function ◽  
Support Vector ◽  
Scientific Publications ◽  
Vector Machines ◽  
Feature Selection Approach ◽  
Sensitivity Specificity ◽  
Excel File

In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. The usefulness of SVM-RCE-R is further supported by development of maTE tool, which uses a similar approach to identify microRNA (miRNA) targets. We have now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to apply and to make it more accessible to the biomedical community. The use of SVM-RCE-R in Knime is simple and intuitive, allowing researchers to immediately begin their data analysis without having to consult an information technology specialist. The input for the Knime tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in the previous version. One of these features is a user-specific ranking function that enables the user to provide the weights of the accuracy, sensitivity, specificity, f-measure, area under curve and precision in the ranking function, allowing the user to select for greater sensitivity or greater specificity as needed. The results show that the ranking function has an impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. This finding motivates future studies to suggest the optimal ranking function.

scholarly journals Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1255
Author(s):  
Malik Yousef ◽  
Burcu Bakir-Gungor ◽  
Amhar Jabeer ◽  
Gokhan Goy ◽  
Rehman Qureshi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Area Under The Curve ◽  
Ranking Function ◽  
Support Vector ◽  
Scientific Publications ◽  
Vector Machines ◽  
Feature Selection Approach ◽  
Sensitivity Specificity ◽  
Excel File

In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R.  SVM-RCE-R, further enhances the capabilities of  SVM-RCE by the addition of  a novel user specified ranking function. This ranking function enables the user to  stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area  under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify microRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics.

scholarly journals Feature Selection for Bankruptcy Prediction

2012 ◽  
pp. 158-178
Author(s):  
A. Gaspar-Cunha ◽  
F. Mendes ◽  
J. Duarte ◽  
A. Vieira ◽  
B. Ribeiro ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Support Vector ◽  
Financial Health ◽  
Vector Machines ◽  
Feature Selection Approach ◽  
A Company

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.

Feature Selection for Bankruptcy Prediction

2010 ◽  
Vol 1 (2) ◽  
pp. 71-91 ◽  
Author(s):  
A. Gaspar-Cunha ◽  
F. Mendes ◽  
J. Duarte ◽  
A. Vieira ◽  
B. Ribeiro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Support Vector ◽  
Financial Health ◽  
Vector Machines ◽  
Selection For ◽  
Feature Selection Approach ◽  
A Company

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.

A feature selection approach for automatic e-book classification based on discourse segmentation

2015 ◽  
Vol 49 (1) ◽  
pp. 2-22
Author(s):  
Jiunn-Liang Guo ◽  
Hei-Chia Wang ◽  
Ming-Way Lai
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Selection Process ◽  
Narrative Structure ◽  
Support Vector ◽  
Content Type ◽  
Digital Documents ◽  
Selection Approach ◽  
Feature Selection Approach

Purpose – The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documents – e-books of online library system. The main idea mainly aims on automatically identifying the discourse features in order to improving the feature selection process rather than focussing on the size of the corpus. Design/methodology/approach – The proposed framework intends to automatically identify the discourse segments within e-books and capture proper discourse subtopics that are cohesively expressed in discourse segments and treating these subtopics as informative and prominent features. The selected set of features is then used to train and perform the e-book classification task based on the support vector machine technique. Findings – The evaluation of the proposed framework shows that identifying discourse segments and capturing subtopic features leads to better performance, in comparison with two conventional feature selection techniques: TFIDF and mutual information. It also demonstrates that discourse features play important roles among textual features, especially for large documents such as e-books. Research limitations/implications – Automatically extracted subtopic features cannot be directly entered into FS process but requires control of the threshold. Practical implications – The proposed technique has demonstrated the promised application of using discourse analysis to enhance the classification of large digital documents – e-books as against to conventional techniques. Originality/value – A new FS technique is proposed which can inspect the narrative structure of large documents and it is new to the text classification domain. The other contribution is that it inspires the consideration of discourse information in future text analysis, by providing more evidences through evaluation of the results. The proposed system can be integrated into other library management systems.

scholarly journals Optimization of the ANOVA Procedure for Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 5160-5165
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Execution Time ◽  
Computing Time ◽  
Mixed Integer ◽  
Support Vector ◽  
Support Vector Classifier ◽  
Vector Machines ◽  
Selection Approach ◽  
Feature Selection Approach

Feature selection is a powerful tool to identify the important characteristics of data for prediction. Feature selection, therefore, can be a tool for avoiding overfitting, improving prediction accuracy and reducing execution time. The applications of feature selection procedures are particularly important in Support vector machines, which is used for prediction in large datasets. The larger the dataset, the more computationally exhaustive and challenging it is to build a predictive model using the support vector classifier. This paper investigates how the feature selection approach based on the analysis of variance (ANOVA) can be optimized for Support Vector Machines (SVMs) to improve its execution time and accuracy. We introduce new conditions on the SVMs prior to running the ANOVA to optimize the performance of the support vector classifier. We also establish the bootstrap procedure as alternative to cross validation to perform model selection. We run our experiments using popular datasets and compare our results to existing modifications of SVMs with feature selection procedure. We propose a number of ANOVA-SVM modifications which are simple to perform, while at the same time, boost significantly the accuracy and computing time of the SVMs in comparison to existing methods like the Mixed Integer Linear Feature Selection approach.

Multitarget Classifiers for Mining in Bioinformatics

2010 ◽  
pp. 684-693
Author(s):  
Diego Liberati
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Minimum Description Length ◽  
Selection Procedure ◽  
Support Vector ◽  
Classification Algorithms ◽  
Small Cardinality ◽  
Feature Selection Approach ◽  
Definition Of ◽  
The Web

Building effective multitarget classifiers is still an on-going research issue: this chapter proposes the use of the knowledge gleaned from a human expert as a practical way for decomposing and extend the proposed binary strategy. The core is a greedy feature selection approach that can be used in conjunction with different classification algorithms, leading to a feature selection process working independently from any classifier that could then be used. The procedure takes advantage from the Minimum Description Length principle for selecting features and promoting accuracy of multitarget classifiers. Its effectiveness is asserted by experiments, with different state-of-the-art classification algorithms such as Bayesian and Support Vector Machine classifiers, over dataset publicly available on the Web: gene expression data from DNA micro-arrays are selected as a paradigmatic example, containing a lot of redundant features due to the large number of monitored genes and the small cardinality of samples. Therefore, in analysing these data, like in text mining, a major challenge is the definition of a feature selection procedure that highlights the most relevant genes in order to improve automatic diagnostic classification.

scholarly journals Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eiman Alothali ◽  
Kadhim Hayawi ◽  
Hany Alashwal
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Support Vector ◽  
Detection Systems ◽  
Vector Machines ◽  
Selection Approach ◽  
Feature Selection Approach

AbstractThe last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).

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