scholarly journals A Comparative Analysis of Biomarker Selection Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Nicoletta Dessì ◽  
Emanuele Pascariello ◽  
Barbara Pes
Keyword(s):  
Feature Selection ◽  
Comparative Analysis ◽  
Biomarker Discovery ◽  
Pathological Condition ◽  
Predictive Performance ◽  
Two Dimensions ◽  
Gene Sets ◽  
Highly Correlated ◽  
Feature Selection Techniques

Feature selection has become the essential step in biomarker discovery from high-dimensional genomics data. It is recognized that different feature selection techniques may result in different set of biomarkers, that is, different groups of genes highly correlated to a given pathological condition, but few direct comparisons exist which quantify these differences in a systematic way. In this paper, we propose a general methodology for comparing the outcomes of different selection techniques in the context of biomarker discovery. The comparison is carried out along two dimensions: (i) measuring the similarity/dissimilarity of selected gene sets; (ii) evaluating the implications of these differences in terms of both predictive performance and stability of selected gene sets. As a case study, we considered three benchmarks deriving from DNA microarray experiments and conducted a comparative analysis among eight selection methods, representatives of different classes of feature selection techniques. Our results show that the proposed approach can provide useful insight about the pattern of agreement of biomarker discovery techniques.

Efficient n-gram construction for text categorization using feature selection techniques

2021 ◽  
Vol 25 (3) ◽  
pp. 509-525
Author(s):  
Maximiliano García ◽  
Sebastián Maldonado ◽  
Carla Vairetti
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Text Categorization ◽  
A Priori ◽  
Predictive Performance ◽  
Online Reviews ◽  
Additional Advantage ◽  
Novel Approach ◽  
N Gram ◽  
Feature Selection Techniques

In this paper, we present a novel approach for n-gram generation in text classification. The a-priori algorithm is adapted to prune word sequences by combining three feature selection techniques. Unlike the traditional two-step approach for text classification in which feature selection is performed after the n-gram construction process, our proposal performs an embedded feature elimination during the application of the a-priori algorithm. The proposed strategy reduces the number of branches to be explored, speeding up the process and making the construction of all the word sequences tractable. Our proposal has the additional advantage of constructing a low-dimensional dataset with only the features that are relevant for classification, that can be used directly without the need for a feature selection step. Experiments on text classification datasets for sentiment analysis demonstrate that our approach yields the best predictive performance when compared with other feature selection approaches, while also facilitating a better understanding of the words and phrases that explain a given task; in our case online reviews and ratings in various domains.

scholarly journals Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models

2021 ◽  
Vol 17 (3) ◽  
pp. e1008831
Author(s):  
Denis A. Shah ◽  
Erick D. De Wolf ◽  
Pierce A. Paul ◽  
Laurence V. Madden
Keyword(s):  
Predictive Accuracy ◽  
Predictive Performance ◽  
Penalized Regression ◽  
Weighted Averaging ◽  
Head Blight ◽  
Weighted Model ◽  
Highly Correlated ◽  
Disease Epidemics ◽  
Better Than

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.

scholarly journals Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1564 ◽  
Author(s):  
Patrick Bradley ◽  
Sina Keller ◽  
Martin Weinmann
Keyword(s):  
Feature Selection ◽  
Dimensionality Reduction ◽  
Hyperspectral Data ◽  
Training Data ◽  
High Dimensional ◽  
Unsupervised Feature Selection ◽  
Feature Selection Techniques ◽  
The Given

In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. The achieved classification results reveal that involving supervised feature selection techniques leads to similar classification results as involving unsupervised feature selection techniques, while the latter perform feature selection independently from the given classification task and thus deliver generally versatile features.

scholarly journals Comparative Analysis of Feature Selection Techniques for Malicious Website Detection in SMOTE Balanced Data

2021 ◽  
Author(s):  
NAMAN BHOJ ◽  
Ashutosh Tripathi ◽  
Granth Singh Bisht ◽  
Adarsh Raj Dwivedi ◽  
Bishwajeet Pandey ◽  
...  
Keyword(s):  
Feature Selection ◽  
Comparative Analysis ◽  
Feature Selection Techniques

scholarly journals A Comparative Study of Multivariate Analysis Techniques for Highly Correlated Variable Identification and Management

2019 ◽  
Vol 5 (1) ◽  
pp. 45-55
Author(s):  
K. Ntotsis ◽  
E. N. Kalligeris ◽  
A. Karagrigoriou
Keyword(s):  
Multivariate Analysis ◽  
Selection Criteria ◽  
Annual Data ◽  
Short Term ◽  
Analysis Techniques ◽  
Correlated Variable ◽  
Highly Correlated ◽  
Feature Selection Techniques

In this work we attempt is to locate and analyze via multivariate analysis techniques, highly correlated covariates (factors) which are interrelated with the Gross Domestic Product and therefore are affecting either on short-term or on long-term its shaping. For the analysis, feature selection techniques and model selection criteria are used. The case study focuses on annual data for Greece for the period 1980-2018.

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