scholarly journals Comparison of the feature selection algorithm in educational data mining

2021 ◽  
Vol 19 (6) ◽  
pp. 1865
Author(s):  
Agung Triayudi ◽  
Iskandar Fitri
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Educational Data Mining ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

A NOVEL FEATURE SELECTION ALGORITHM WITH SUPERVISED MUTUAL INFORMATION FOR CLASSIFICATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350027
Author(s):  
JAGANATHAN PALANICHAMY ◽  
KUPPUCHAMY RAMASAMY
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Class A ◽  
Selection Algorithms ◽  
The Relationship ◽  
Class Variable

Feature selection is essential in data mining and pattern recognition, especially for database classification. During past years, several feature selection algorithms have been proposed to measure the relevance of various features to each class. A suitable feature selection algorithm normally maximizes the relevancy and minimizes the redundancy of the selected features. The mutual information measure can successfully estimate the dependency of features on the entire sampling space, but it cannot exactly represent the redundancies among features. In this paper, a novel feature selection algorithm is proposed based on maximum relevance and minimum redundancy criterion. The mutual information is used to measure the relevancy of each feature with class variable and calculate the redundancy by utilizing the relationship between candidate features, selected features and class variables. The effectiveness is tested with ten benchmarked datasets available in UCI Machine Learning Repository. The experimental results show better performance when compared with some existing algorithms.

scholarly journals An Analysis of Feature Selection Algorithm and their Optimization - A Scrutiny

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 01-20
Author(s):  
S. Bharani Nayagi ◽  
T.S. Shiny Angel
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Selection Mechanism ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Processing Techniques

The eradication of correlated evidence of the enormous volume of the directory is designated as data mining. Extracting discriminate knowledge associate with the approach is performed by a feature of knowledge. Knowledge rejuvenation is carried out as features and the process is delineated as a feature selection mechanism. Feature selection is a subset of features, acquired more information. Before data mining, Feature selection is essential to trim down the elevated dimensional information. Without feature selection pre-processing techniques, classification required interminable calculation duration which might lead to intricacy. The foremost intention of the analysis is to afford a summary of feature selection approaches adopted to evaluate the extreme extensive features.

scholarly journals Machine Learning Based Supervised Feature Selection Algorithm for Data Mining

2019 ◽  
Vol 8 (10) ◽  
pp. 3396-3401 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Modern World ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Minimum Number ◽  
Preprocessing Technique

Data Scientists focus on high dimensional data to predict and reveal some interesting patterns as well as most useful information to the modern world. Feature Selection is a preprocessing technique which improves the accuracy and efficiency of mining algorithms. There exist a numerous feature selection algorithms. Most of the algorithms failed to give better mining results as the scale increases. In this paper, feature selection for supervised algorithms in data mining are considered and given an overview of existing machine learning algorithm for supervised feature selection. This paper introduces an enhanced supervised feature selection algorithm which selects the best feature subset by eliminating irrelevant features using distance correlation and redundant features using symmetric uncertainty. The experimental results show that the proposed algorithm provides better classification accuracy and selects minimum number of features.

scholarly journals A Systematic Data Mining Method for Clustering of Data using Map-Reduce Model

2020 ◽  
Vol 9 (3) ◽  
pp. 716-720
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Map Reduce ◽  
Mining Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Selection Approach ◽  
Research Concept ◽  
Future Data ◽  
Feature Selection Approach

Data mining is an important research concept that has a vast scope in future. Data mining is used to find the unseen information from the data. In cluster, main half is feature choice. It involves recognition of a set of options of a set, because feature choice is taken into account as a necessary method. They additionally produce the approximate and according requests with the initial set of options employed in this kind of approach. The most construct on the far side this paper is to relinquish the end result of the bunch options. This paper conveys the cluster and the clustering process. The processing of large datasets the nature of clustering where some more concepts are more helpful and important in a clustering process. In clustering methodology many concepts are very useful. The feature selection algorithm which affects the entire process of clustering is the map-reduce concept. Here time needed to seek out the effective options, options of quality subsets is capable of providing effectiveness. The paper discussed map-reduce feature selection approach, its algorithm and framework of implementation.

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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