Add to ORKGIdentifying a small number of features that can represent the data is believed to be NP-hard. Previous approaches exploit algebraic structure and use randomization. We propose an algorithm based on ideas similar to the Weighted A* algorithm in heuristic search. Our experiments show this new algorithm to be more accurate than the current state of the art
Feature selection is a key problem to pattern recognition. So far, most methods of feature selection...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In this paper, we consider an Integer Programming (IP) model for a particular class of Feature Selec...
Identifying a small number of features that can represent the data is a known problem that comes up ...
The problem of selecting a subset of relevant features in a potentially overwhelming quantity of dat...
Feature selection methods are used in machine learning and data analysis to select a subset of featu...
Feature selection methods are used in machine learning and data analysis to select a subset of featu...
The A* algorithm is a well-known heuristic best-first search method. Several performance-accelerat...
Feature Selection (FS) arises in data analysis to reduce the dimension of large data. We focus on in...
The following are two classical approaches to dimensionality reduction: 1. Approximating the data wi...
In supervised learning scenarios, feature selection has been studied widely in the literature. Selec...
Feature selection, also known as attribute selection, is the technique of selecting a subset of rele...
Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of co...
This document is the Accepted Manuscript version of the following article: Deepak Panday, Renato Cor...
We introduce a new model addressing feature selection from a large dictionary of variables that can ...
Feature selection is a key problem to pattern recognition. So far, most methods of feature selection...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In this paper, we consider an Integer Programming (IP) model for a particular class of Feature Selec...
Identifying a small number of features that can represent the data is a known problem that comes up ...
The problem of selecting a subset of relevant features in a potentially overwhelming quantity of dat...
Feature selection methods are used in machine learning and data analysis to select a subset of featu...
Feature selection methods are used in machine learning and data analysis to select a subset of featu...
The A* algorithm is a well-known heuristic best-first search method. Several performance-accelerat...
Feature Selection (FS) arises in data analysis to reduce the dimension of large data. We focus on in...
The following are two classical approaches to dimensionality reduction: 1. Approximating the data wi...
In supervised learning scenarios, feature selection has been studied widely in the literature. Selec...
Feature selection, also known as attribute selection, is the technique of selecting a subset of rele...
Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of co...
This document is the Accepted Manuscript version of the following article: Deepak Panday, Renato Cor...
We introduce a new model addressing feature selection from a large dictionary of variables that can ...
Feature selection is a key problem to pattern recognition. So far, most methods of feature selection...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In this paper, we consider an Integer Programming (IP) model for a particular class of Feature Selec...