Machine Learning Algorithms

Dhanda, Namrata; Datta, Stuti Shukla; Dhanda, Mudrika

doi:10.4018/978-1-5225-7955-7.ch009

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Machine learning (ML) is a subset of artificial intelligence (AI) that gives frameworks the capacity to naturally take in and improve for a fact without being unequivocally modified [12]. There are three machine learning algorithms which are supervised learning, unsupervised learning and reinforcement learning.…”

Section: B Machine Learning (Ml)mentioning

confidence: 99%

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

Abidin¹,

Akmal²,

Mohd³

et al. 2020

IJACSA

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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.

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Section: B Machine Learning (Ml)mentioning

confidence: 99%

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

Abidin¹,

Akmal²,

Mohd³

et al. 2020

IJACSA

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show abstract

“…Here, the probability of a target class A can be found if B (predictors or features) are given. This shows that the features or predictors are independent of one other [67], [68].…”

Section: ) Naive Bayes Classifiermentioning

confidence: 80%

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

2021

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Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

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“…The algorithm, in this case, receives a set of input variables that are used to predict a response, and a comparison of the model output with the correct one is responsible for correcting the parameter. 90 Unsupervised learning by itself does not receive labeled data. This procedure is convenient to discover a way to group, or cluster, a set of elements by its similarity.…”

Section: Reaction Chemistry and Engineering Reviewmentioning

confidence: 99%

Combining computational fluid dynamics, photon fate simulation and machine learning to optimize continuous-flow photocatalytic systems

et al. 2023

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Machine Learning Algorithms

Cited by 8 publications

References 2 publications

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

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

Combining computational fluid dynamics, photon fate simulation and machine learning to optimize continuous-flow photocatalytic systems

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