Novel feature ranking criteria for interval valued feature selection

Background: Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer’s disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. Objective: This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Results: Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. Conclusion: The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

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Intra-Inter Feature Ranking based Feature Selection Method for Bearing Fault Classification

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt45670.2019.8944884 ◽

2019 ◽

Author(s):

Sandeep S. Udmale ◽

Sanjay Kumar Singh

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Fault Classification ◽

Feature Ranking ◽

Bearing Fault

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Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

IEEE Access ◽

10.1109/access.2019.2947701 ◽

2019 ◽

Vol 7 ◽

pp. 151482-151492 ◽

Cited By ~ 5

Author(s):

Anwar Ul Haq ◽

Defu Zhang ◽

He Peng ◽

Sami Ur Rahman

Keyword(s):

Feature Selection ◽

Feature Ranking ◽

Multiple Feature ◽

Clustering Of Variables

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A NEW ENSEMBLE METHOD FOR FEATURE RANKING IN TEXT MINING

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213013500103 ◽

2013 ◽

Vol 22 (03) ◽

pp. 1350010 ◽

Cited By ~ 6

Author(s):

SABEREH SADEGHI ◽

HAMID BEIGY

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Dimensionality Reduction ◽

Ensemble Methods ◽

Ease Of Use ◽

Convergence Time ◽

Feature Ranking ◽

Initial Population ◽

Feature Subset ◽

Ranking Methods

Dimensionality reduction is a necessary task in data mining when working with high dimensional data. A type of dimensionality reduction is feature selection. Feature selection based on feature ranking has received much attention by researchers. The major reasons are its scalability, ease of use, and fast computation. Feature ranking methods can be divided into different categories and may use different measures for ranking features. Recently, ensemble methods have entered in the field of ranking and achieved more accuracy among others. Accordingly, in this paper a Heterogeneous ensemble based algorithm for feature ranking is proposed. The base ranking methods in this ensemble structure are chosen from different categories like information theoretic, distance based, and statistical methods. The results of the base ranking methods are then fused into a final feature subset by means of genetic algorithm. The diversity of the base methods improves the quality of initial population of the genetic algorithm and thus reducing the convergence time of the genetic algorithm. In most of ranking methods, it's the user's task to determine the threshold for choosing the appropriate subset of features. It is a problem, which may cause the user to try many different values to select a good one. In the proposed algorithm, the difficulty of determining a proper threshold by the user is decreased. The performance of the algorithm is evaluated on four different text datasets and the experimental results show that the proposed method outperforms all other five feature ranking methods used for comparison. One advantage of the proposed method is that it is independent to the classification method used for classification.

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