A review of feature selection methods based on mutual information
JR Vergara, PA Estévez - Neural computing and applications, 2014 - Springer
In this work, we present a review of the state of the art of information-theoretic feature
selection methods. The concepts of feature relevance, redundance, and complementarity …
selection methods. The concepts of feature relevance, redundance, and complementarity …
[BOOK][B] Statistical pattern recognition
AR Webb - 2003 - books.google.com
Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …
many advances in recent years. New andemerging applications-such as data mining, web …
A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification
In recent years, sentiment analysis is becoming more and more important as the number of
digital text resources increases in parallel with the development of information technology …
digital text resources increases in parallel with the development of information technology …
[PDF][PDF] A review paper on feature selection methodologies and their applications
Feature selection is the process of eliminating features from the data set that are irrelevant
with respect to the task to be performed. Feature selection is important for many reasons …
with respect to the task to be performed. Feature selection is important for many reasons …
Feature selection via mutual information: New theoretical insights
Mutual information has been successfully adopted in filter feature-selection methods to
assess both the relevancy of a subset of features in predicting the target variable and the …
assess both the relevancy of a subset of features in predicting the target variable and the …
Efficient feature selection method using real-valued grasshopper optimization algorithm
Feature selection is the problem of finding the minimum number of features among a
redundant feature space which leads to the maximum classification performance. In this …
redundant feature space which leads to the maximum classification performance. In this …
Feature selection for high-dimensional data—a Pearson redundancy based filter
An algorithm for filtering information based on the Pearson χ 2 test approach has been
implemented and tested on feature selection. This test is frequently used in biomedical data …
implemented and tested on feature selection. This test is frequently used in biomedical data …
Feature selection based on the SVM weight vector for classification of dementia
Computer-aided diagnosis of dementia using a support vector machine (SVM) can be
improved with feature selection. The relevance of individual features can be quantified from …
improved with feature selection. The relevance of individual features can be quantified from …
A new semantic-based feature selection method for spam filtering
The Internet emerged as a powerful infrastructure for the worldwide communication and
interaction of people. Some unethical uses of this technology (for instance spam or viruses) …
interaction of people. Some unethical uses of this technology (for instance spam or viruses) …
[BOOK][B] Reconstruction and analysis of 3D scenes
M Weinmann - 2016 - Springer
The fully automatic processing and analysis of 3D point clouds represents a topic of major
interest in the fields of photogrammetry, remote sensing, computer vision, and robotics …
interest in the fields of photogrammetry, remote sensing, computer vision, and robotics …