A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks

Yan, Jianjian; Zhang, Zhongnan; Lin, Kwei-Jay; Yang, Fan; Luo, Xiangfeng

doi:10.1016/j.knosys.2020.105922

Cited by 36 publications

(12 citation statements)

References 38 publications

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“…The experiments performed using a data split technique with 70% of training data and 30% for testing dataset. The results were conducted by testing different problem transformation and adaptive techniques such as OvA, BR, LP, CC, and adaptive ML -KNN with different classification algorithms such as SVC [46, and 47], LR [48], RF [49], Gaussian NB [50], and DT [51].…”

Section: Resultsmentioning

confidence: 99%

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

et al. 2020

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Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members' to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members' devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes' assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students' learning experiences.

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Section: Resultsmentioning

confidence: 99%

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

et al. 2020

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“…Class separability, class overlaps, and imbalances between and within classes are the most mentioned problems for multi-class classifications [ 243 ]. Patterns that are nonlinear and unseen in the dataset can add more complexity to multi-class categorisation [ 244 ].…”

Section: Substantial Analysismentioning

confidence: 99%

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

Abdulwahid

Mahmoud

Zaidan

et al. 2022

IJERPH

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With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.

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“…A problem can be solved by using an ensemble decision tree [55]. Constructing a decision tree is usually a recursive procedure, where a function is repeatedly optimized and training data are partitioned into the root and internal nodes until a termination condition is met [56]. Usually, the termination condition is the logic disjunction of several stopping predicates that account for different kinds of imposed limitations, for example, on the branch length, on the possible information gain [57].…”

Section: The Background Of the Researchmentioning

confidence: 99%

Decision Tree and AHP Methods Application for Projects Assessment: A Case Study

et al. 2021

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This research is dedicated to the modelling of decision process occurring during the implementation of construction projects. Recent studies generally do not assess the robustness of the decisions regarding the possible changes during the construction project implementation. However, such an assessment might increase the reliability of the decision-making process. We addressed this gap through a new model that combines the decision-making process modelling with the AHP method and includes the analysis of model stability concerning stakeholders’ behaviour. We used the Analytic Hierarchy Process (AHP) and Decision tree methods to model the decision-making process. The proposed model was validated on a case study of multiple construction projects. The assessment was performed from individual investor’s and independent expert’s perspectives. The criteria for the assessment were selected according to the principles of sustainability. We performed the sensitivity analysis, making it possible to assess the possible changes of the decisions depending on the potential patterns of the decision-makers’ behaviour. The results of the study show that, sometimes, small fluctuations in the project factors affect the project selection indicating the possible lack of the robustness of the project decisions.

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A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks

Cited by 36 publications

References 38 publications

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

Decision Tree and AHP Methods Application for Projects Assessment: A Case Study

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