Forward Iterative Feature Selection Based on Laplacian Score

2020 ◽  
pp. 381-392
Author(s):  
Qing-Qing Pang ◽  
Li Zhang
Keyword(s):  
Feature Selection ◽  
Laplacian Score

Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection

2015 ◽  
Vol 49 (3) ◽  
pp. 1161-1185 ◽  
Author(s):  
Khalid Benabdeslem ◽  
Haytham Elghazel ◽  
Mohammed Hindawi
Keyword(s):  
Feature Selection ◽  
Laplacian Score

scholarly journals Isolation-based feature Selection for Unsupervised Outlier Detection

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Qibo Yang ◽  
Jaskaran Singh ◽  
Jay Lee
Keyword(s):  
Feature Selection ◽  
Outlier Detection ◽  
Simulated Data ◽  
Support Vector ◽  
Detection Algorithms ◽  
Complex Interactions ◽  
Laplacian Score ◽  
High Dimensional Datasets ◽  
Isolation Forest ◽  
Unsupervised Outlier Detection

For high-dimensional datasets, bad features and complex interactions between features can cause high computational costs and make outlier detection algorithms inefficient. Most feature selection methods are designed for supervised classification and regression, and limited works are specifically for unsupervised outlier detection. This paper proposes a novel isolation-based feature selection (IBFS) method for unsupervised outlier detection. It is based on the training process of isolation forest. When a point of a feature is used to split the data, the imbalanced distribution of split data is measured and used to quantify how strong this feature can detect outliers. We also compare the proposed method with variance, Laplacian score and kurtosis. These methods are benchmarked on simulated data to show their characteristics. Then we evaluate the performance using one-class support vector machine, isolation forest and local outlier factor on several real-word datasets. The evaluation results show that the proposed method can improve the performance of isolation forest, and its results are similar to and sometimes better than another useful outlier indicator: kurtosis, which demonstrate the effectiveness of the proposed method. We also notice that sometimes variance and Laplacian score has similar performance on the datasets.

Classification of EEG Signals Using Filter Bank Common Spatial Pattern Based on Fisher and Laplacian Criteria

2012 ◽  
Vol 239-240 ◽  
pp. 1033-1038
Author(s):  
Qing Guo Wei ◽  
Bin Wan ◽  
Zong Wu Lu
Keyword(s):  
Feature Selection ◽  
Spatial Pattern ◽  
Filter Bank ◽  
Selection Criterion ◽  
Classification Performance ◽  
Fisher Score ◽  
Common Spatial Pattern ◽  
Frequency Bands ◽  
Operational Frequency ◽  
Laplacian Score

Common spatial pattern (CSP) is a highly successful algorithm in motor imagery based brain-computer interfaces (BCIs). The performance of the algorithm, however, depends largely on the operational frequency bands. To address the problem, a filter bank was applied to find optimal frequency bands. In filter bank, CSP was applied in all sub-band signals for feature extraction. The feature selection is the key of filter bank method for increasing classification performance. In this study, coefficient decimation (CD) technique was used to devise filter bank, while Fisher score and Laplacian score were proposed as feature selection criterion. In off-line analysis, the proposed method yielded relatively better cross-validation classification accuracies.

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