Feature Selection
Feature selection with simple ANN ensembles
... in feature selection is to evaluate the performance of different methods using error curves that shows the resulting average classification error as a function of the number of variables ...complete ...
10
Feature selection in pathological voice classification using dinamyc of component analysis
... dynamic feature sets that are suitable for classification of pathological voices using ...the feature selection methodology presented in [10], is ...
5
Two new feature selection algorithms with rough sets theory
... other feature selection methods implemented with Pattern Recognition (PR) [Alv05], Estimation of Distribution Algorithms (EDA) (epigraph 2) and Ant Colony Optimization Algorithms (ACO) ...
8
TítuloPrediction of high anti angiogenic activity peptides in silico using a generalized linear model and feature selection
... The best model in terms of both AUC (see Fig. 4a) and accuracy (see Fig. 4c) has been the glmnet algorithm. The algorithm was trained with the union of the three datasets (AAC, DC and TC) and only the 200 features with ...
11
TítuloKernel based feature selection techniques for transport proteins based on star graph topological indices
... for feature selection using SVMs known as Support Vector Machine Recursive Feature Elimination (mSVM-RFE), the output is a feature ...the feature with the lowest ...the feature ...
18
Classification of Imaginary motor task from Electroencephalographic Signals: A Comparison of Feature Selection Methods and Classification Algorithms
... presented. Feature extraction is performed by Short Time Fourier Transform, providing a representation of the brain signals in the time-frequency ...domain. Feature selec- tion methods based on Principal ...
10
Bootstrap Feature Selection in Support Vector Machines for Ventricular Fibrillation Detection
... dimensional feature space makes the input feature selection a difficult task to be ...space feature set that still holds the classification performance of the complete ...input feature ...
6
TítuloA comparison of performance of K complex classification methods using feature selection
... a feature-based detection approach is ...of feature selection ...features, feature selection methods were employed. Feature selection is arguably the most popular ...
14
DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
... Feature selection is area of fundamental interest for machine learning. Feature se- lection techniques choose a subset of input variables (features) by eliminating redun- dant and irrelevant ...and ...
10
TítuloNovel feature selection methods for high dimensional data
... As was explained above, the evaluation of the feature selection methods is done by counting the number of correct/wrong features. However, it is also interesting and a common practice in the literature ...
372
Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city
... our feature selection method. In order to deal with feature selection methods with large set of features, classic methods can not be ...address feature selection achieving very ...
10
A survey of feature selection in Internet traffic characterization
... of feature sets has raised from tens to thousands or even ...big feature set could be computationally expensive; furthermore, irrelevant and re- dundant features may also decrease the accuracy of ...
22
A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning
... model selection was proposed by Seeger [19], where a maximum a posteriori (MAP) criterion on the parameters is imposed using a vari- ational ...the feature selection problem in connection with ...
33
Un algoritmo de evolución diferencial para la selección de características en el análisis de sentimientosA diferential evolution algorithm for feature selection in sentiment analysis
... as feature extraction and may include sets of words, phrase patterns, and ...the feature space makes it desirable to implement feature se- lection previous to the classication, improving classication ...
100
Expert knowledge-guided feature selection for data-based industrial process monitoring
... method based on Hausdorff distance measure in a supervised manner. Fraleigth et al. [6] developed a sensor system selection for model-based real-time optimization. Verron et al. [7] proposed supervised ...
14
TítuloImproving enzyme regulatory protein classification by means of SVM RFE feature selection
... the selection was made with an identity of less than 20% (similarity among two different sequences), resolution of ...the selection guarantee that the proteins do not have any other possible biological ...
16
TítuloNovel machine learning methods based on information theory
... cost-based feature selection, trying to balance the correlation of the features with the class and their ...to feature extraction, in [155] a criterion is proposed to select kernel parameters based ...
138
TítuloAdvancing the diagnosis of dry eye syndrome : development of automated assessments of tear film lipid layer patterns
... a feature extraction ...whole feature vector has to be calculated and so there is no reduction in ...ner, feature selection techniques are applied and so, when an input is decided to be ...
191
Empirical study of dimensionality reduction methodologies for classification problems
... In figure 147 we can appreciate that the Auc mean for the pair Gradient Boosting Classifier – Univariate Feature Selection makes a “fair” discrimination with a value of 0.75. We can find four phases. In the ...
195
TítuloNew scalable machine learning methods: beyond classification and regression
... Some feature selection algorithms, such as InfoGain, require the attributes of the dataset to be discrete. This specification often forces the user to preprocess the dataset in order to obtain a modified ...
174
