scholarly journals An Examination of Relationship between Career Maturity and Multiple Factors by Feature Selection

2020 ◽  
Author(s):  
Shuxing Zhang ◽  
Qinneng Xu
Keyword(s):  
Feature Selection ◽  
Career Maturity ◽  
Selection Methods ◽  
School Students ◽  
Feasible Method ◽  
Multiple Factors ◽  
Sample Data ◽  
Forward Stepwise ◽  
The Relationship ◽  
Significant Factors

The purpose of this study is to investigate the relationship between career maturity and a branch of factors among senior school students. The sample data were collected from a total of 189 students. The linear relationship between career maturity and 72 factors were tested by using feature selection methods. LASSO and forward stepwise were compared based on crossvalidation. The results showed that LASSO was a feasible method to select the significant factors, and 12 of the total 72 factors were found to be important in predicting career maturity.

scholarly journals Analysis of Factors Influencing Creative Personality of Elementary School Students

2017 ◽  
Vol 10 (5) ◽  
pp. 167 ◽  
Author(s):  
Jongman Park ◽  
Minkee Kim ◽  
Shinho Jang
Keyword(s):  
Quantitative Research ◽  
Sixth Grade ◽  
Fifth Grade ◽  
Science Class ◽  
Scientific Attitude ◽  
School Students ◽  
Positive Effects ◽  
Gender And Age ◽  
The Relationship ◽  
Significant Factors

This quantitative research examined factors that affect elementary students’ creativity and how those factors correlate. Aiming to identify significant factors that affect creativity and to clarify the relationship between these factors by path analysis, this research was designed to be a stepping stone for creativity enhancement studies. Data were gathered from 208 students in 3 fifth-grade classes and 3 sixth-grade classes in 5 different schools located in Seoul, Korea. Survey questions, asked through five-score Likert-scale items, focused on attentiveness in science class, creativity and scientific attitude, which has been shown by the literature to have positive influences on one another. The findings include that their scientific attitude, attentiveness, and creativity correlated with significance, where gender did not have an effect on the relationship. Gender and age of the students have shown no significant effect on their scientific attitude, attentiveness or creativity. Scientific attitude, attentiveness and creativity have demonstrated positive effects to each other, the effect being stronger from scientific attitude to creativity (0.659) than the other two, attentiveness & scientific attitude (0.32) and attentiveness & creativity (0.368). Scientific attitude affects creativity most directly (0.659), and attentiveness would affect creativity more as a cofactor next to the scientific attitude (0.213) rather than when it’s by itself (0.154). That is, if a teacher devises a certain way to enhance attentiveness of students during their science class, their scientific attitude and attentiveness would increase, giving them a solid chance to enhance their creativity consequently.

scholarly journals Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chengyuan Huang
Keyword(s):  
Feature Selection ◽  
Measurement Method ◽  
Feature Selection Method ◽  
Selection Method ◽  
Small Sample ◽  
High Dimensional ◽  
Selection Methods ◽  
Sample Data ◽  
Stability Measurement ◽  
The Stability

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

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