scholarly journals Feature subset selection in text-learning

1998 ◽  
pp. 95-100 ◽  
Author(s):  
Dunja Mladenić
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Text Learning

Estimation of Distribution Algorithms for Feature Subset Selection in Large Dimensionality Domains

Data Mining ◽  
2011 ◽  
pp. 97-116 ◽  
Author(s):  
Inaki Inza ◽  
Pedro Larranaga ◽  
Basilio Sierra
Keyword(s):  
Probabilistic Models ◽  
Subset Selection ◽  
Population Based ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution Algorithms ◽  
Text Learning ◽  
Estimation Of Distribution ◽  
Selection Tasks ◽  
Distribution Algorithms

Feature Subset Selection (FSS) is a well-known task of Machine Learning, Data Mining, Pattern Recognition or Text Learning paradigms. Genetic Algorithms (GAs) are possibly the most commonly used algorithms for Feature Subset Selection tasks. Although the FSS literature contains many papers, few of them tackle the task of FSS in domains with more than 50 features. In this chapter we present a novel search heuristic paradigm, called Estimation of Distribution Algorithms (EDAs), as an alternative to GAs, to perform a population-based and randomized search in datasets of a large dimensionality. The EDA paradigm avoids the use of genetic crossover and mutation operators to evolve the populations. In absence of these operators, the evolution is guaranteed by the factorization of the probability distribution of the best solutions found in a generation of the search and the subsequent simulation of this distribution to obtain a new pool of solutions. In this chapter we present four different probabilistic models to perform this factorization. In a comparison with two types of GAs in natural and artificial datasets of a large dimensionality, EDAbased approaches obtain encouraging results with regard to accuracy, and a fewer number of evaluations were needed than used in genetic approaches.

A New Approach for Feature Subset Selection using Quantum Inspired Owl Search Algorithm

2020 ◽  
Author(s):  
Ashis Kumar Mandal ◽  
Rikta Sen ◽  
Saptarsi Goswami ◽  
Amlan Chakrabarti ◽  
Basabi Chakraborty
Keyword(s):  
Search Algorithm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
New Approach

scholarly journals Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree

2021 ◽  
Vol 6 (3) ◽  
pp. 177
Author(s):  
Muhamad Arief Hidayat
Keyword(s):  
Ensemble Learning ◽  
Classification Tree ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Pregnancy Risk ◽  
Risk Status ◽  
Cost Sensitive Learning ◽  
Best Value ◽  
Selection Stage

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values ​​of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values ​​are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%

scholarly journals IFSS An Improved Filter-Wrapper Algorithm for Feature Subset Selection

2014 ◽  
Vol 95 (14) ◽  
pp. 33-35
Author(s):  
Saurabh Soni ◽  
Pratik Patel
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset
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