Research on Application of Machine Learning in Data Mining

Teng, Xiuyi; Yan, Gao

doi:10.1088/1757-899x/392/6/062202

Cited by 32 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is a well-known technique that is broadly utilised by researchers for personality prediction. Due to the advantages of machine learning in learning historical data and making a prediction on future data, the researcher can also use it for learning personality patterns [1]. Such an application is also well-known in psychological science as an assessment tool to predict personality.…”

Section: Introductionmentioning

confidence: 99%

Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

Abidin¹,

Akmal²,

Mohd³

et al. 2020

IJACSA

View full text Add to dashboard Cite

The term "personality" can be defined as the mixture of features and qualities that built an individual's distinctive characters, including thinking, feeling and behaviour. Nowadays, it is hard to select the right employees due to the vast pool of candidates. Traditionally, a company will arrange interview sessions with prospective candidates to know their personalities. However, this procedure sometimes demands extra time because the total number of interviewers is lesser than the total number of job seekers. Since technology has evolved rapidly, personality computing has become a popular research field that provides personalisation to users. Currently, researchers have utilised social media data for auto-predicting personality. However, it is complex to mine the social media data as they are noisy, come in various formats and lengths. This paper proposes a machine learning technique using Random Forest classifier to automatically predict people's personality based on Myers-Briggs Type Indicator® (MBTI). Researchers compared the performance of the proposed method in this study with other popular machine learning algorithms. Experimental evaluation demonstrates that Random Forest classifier performs better than the different three machine learning algorithms in terms of accuracy, thus capable in assisting employers in identifying personality types for selecting suitable candidates.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

Abidin¹,

Akmal²,

Mohd³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Discovering the most adjacent and most distant FoSs in different years can give us a deeper insight into computer science topics. For instance, “Distributed Computing” and “Computer Networks” were the most similar topic pair in computer science studies from 2000 to 2017 in the dataset, but “Machine learning” and “Data mining” became the most adjacent FoS pair in 2018, and we know that due to the large amount of information published in recent years, these two areas have multiple uses for extracting knowledge from unstructured data and are involved in solving numerous problems concurrently (Taranto-Vera et al, 2021 ). In order to provide a comprehensive representation for these research topics, we sorted the FoS pairs in computer science according to their pairwise distances, which resulted in a ranking of FoS pairs.…”

Section: Resultsmentioning

confidence: 99%

An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains

2023

View full text Add to dashboard Cite

The study of topic evolution aims to analyze the behavior of different research fields by utilizing various features such as the relationships between articles. In recent years, many published papers consider more than one field of study which has led to a significant increase in the number of inter-field and interdisciplinary articles. Therefore, we can analyze the similarity/dissimilarity and convergence/divergence of research fields based on topic analysis of the published papers. Our research intends to create a methodology for studying the evolution of the research fields. In this paper, we propose an embedding approach for modeling each research topics as a multidimensional vector. Using this model, we measure the topic’s distances over the years and investigate how topics evolve over time. The proposed similarity metric showed many advantages over other alternatives (such as Jaccard similarity) and it resulted in better stability and accuracy. As a case study, we applied the proposed method to subsets of computer science for experimental purposes, and the results were quite comprehensible and coherent.

show abstract

“…Data mining is a very popular method that uses statistical methods, visualization, machine learning, and other techniques of data manipulation and knowledge extraction to analyze, diagnose, predict and control various diseases, including infectious diseases to gain insight into data and hidden patterns 24–26 . Machine learning as a learning method by automating knowledge acquisition has important applications in AI research 27 . Using various data mining techniques to the data collected about infectious diseases, besides a better understanding of the effective factors in the occurrence of infection, it is possible to recover from these diseases 18 …”

Section: Introductionmentioning

confidence: 99%

“…[24][25][26] Machine learning as a learning method by automating knowledge acquisition has important applications in AI research. 27 Using various data mining techniques to the data collected about infectious diseases, besides a better understanding of the effective factors in the occurrence of infection, it is possible to recover from these diseases. 18 By the spread of COVID-19, using ML algorithms for various applications, 28 including disease diagnosis, 29 prediction of readmission, 30 predicting of survival based on data, specific clinical symptoms, and parameters 21,[31][32][33] and providing of decision-making models 20 were developed.…”

mentioning

confidence: 99%

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Yazdani

Bigdeli

Zahmatkeshan

2023

Health Science Reports

View full text Add to dashboard Cite

Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patients

show abstract

Research on Application of Machine Learning in Data Mining

Cited by 32 publications

References 1 publication

Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Contact Info

Product

Resources

About