Chapter 6 FEATURE SELECTION IN MICROARRAYANALYSISEric
نویسنده
چکیده
منابع مشابه
Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
متن کاملOptimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
متن کاملMental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...
متن کاملA New Framework for Distributed Multivariate Feature Selection
Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...
متن کاملDeveloping a Filter-Wrapper Feature Selection Method and its Application in Dimension Reduction of Gen Expression
Nowadays, increasing the volume of data and the number of attributes in the dataset has reduced the accuracy of the learning algorithm and the computational complexity. A dimensionality reduction method is a feature selection method, which is done through filtering and wrapping. The wrapper methods are more accurate than filter ones but perform faster and have a less computational burden. With ...
متن کامل