scholarly journals A Method for the Automatic Selection of Training Tasks in Learning Environment for IT Students

2020 ◽  
Vol 24 (2) ◽  
pp. 17-28 ◽  
Author(s):  
S. U. Rzheutskaya ◽  
M. V. Kharina
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Learning Process ◽  
Learning Effect ◽  
Training Sample ◽  
Educational Process ◽  
Information Environment ◽  
Level Of Difficulty ◽  
Selection Of

Purpose of research. The research, the results of which are presented in this article, was carried out in order to activate and improve the efficiency of independent work of students in the information environment of learning by rational individual selection of training tasks. In the process of the research, a method for automatically selecting tasks for self-completion was developed and implemented in the educational process, based on predicting the difficulty and learning effect of the task for a specific student, taking into account the complexity of the task and the student’s readiness to perform this task. Methods and materials. The article provides a distinction between the concepts of complexity, difficulty, and the learning effect of training tasks. On this basis, the task of predicting the level of difficulty of the task for the student is set as a task of automatic classification of “student-task" pairs, which represent a set of characteristics of the student and the task that are available in the database of the e-learning system. The result of the classification is a forecast of the level of difficulty of the task for the student, on the basis of which a decision is made about the learning effect of this task.The classification problem is one of the well-developed machine learning tasks “with a lecturer". Decision trees were selected from several well-known trained classification models for implementation, since they, unlike neural networks, represent prediction rules in a visual form, while highlighting significant features. The learning phase of the model consists of building a decision tree based on a training sample containing data on precedents for students to complete tasks. As a result of the computational experiment, decision trees were built for several disciplines that practice automatic verification of students’ decisions, i.e. there is data for forming a training sample.Results. The article provides an example of a decision tree based on a training sample, which is formed on the basis of data from an electronic workshop on the discipline “Foreign language ". The quality of the predictive model was determined on the exam sample by the criteria of accuracy and generalizing ability (the degree of severity of the “retraining effect”). The obtained values of these indicators allow us to recognize the quality as acceptable. The first results ofpractical application of the proposed method of selecting tasks in the educational process are analyzed. The software developed in the process of the research can be considered as the basis of a recommendation system that can not replace live communication between the student and the lecturer, but is their smart assistant in the learning process. Conclusion. In general, the results of the research show that the capabilities of artificial intelligence technologies, in particular, machine learning, allow us to put into practice the principle of individualized learning, to adapt the learning process to the individual characteristics of each student in order to effectively develop their professional competencies. The proposed method is implemented and tested in the information environment of training students of IT areas of Vologda State University, however, this approach is quite universal, and it can be extended to other subject areas and forms of training.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

scholarly journals Analysis of Decision Tree Induction Algorithms

2019 ◽  
Vol 8 (11) ◽  
pp. e298111473
Author(s):  
Hugo Kenji Rodrigues Okada ◽  
Andre Ricardo Nascimento das Neves ◽  
Ricardo Shitsuka
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Quantitative Study ◽  
Data Structures ◽  
Execution Time ◽  
Supervised Machine Learning ◽  
Decision Tree Induction ◽  
Classification And Regression ◽  
Cart Algorithm

Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.

Machine Learning Techniques Applied to Profile Mobile Banking Users in India

2013 ◽  
Vol 5 (1) ◽  
pp. 82-92 ◽  
Author(s):  
M. Carr ◽  
V. Ravi ◽  
G. Sridharan Reddy ◽  
D. Veranna
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Decision Trees ◽  
Multilayer Perceptron ◽  
Machine Learning Techniques ◽  
Mobile Banking ◽  
Classification Rules ◽  
Learning Techniques ◽  
Potential Customers

This paper profiles mobile banking users using machine learning techniques viz. Decision Tree, Logistic Regression, Multilayer Perceptron, and SVM to test a research model with fourteen independent variables and a dependent variable (adoption). A survey was conducted and the results were analysed using these techniques. Using Decision Trees the profile of the mobile banking adopter’s profile was identified. Comparing different machine learning techniques it was found that Decision Trees outperformed the Logistic Regression and Multilayer Perceptron and SVM. Out of all the techniques, Decision Tree is recommended for profiling studies because apart from obtaining high accurate results, it also yields ‘if–then’ classification rules. The classification rules provided here can be used to target potential customers to adopt mobile banking by offering them appropriate incentives.

SIZES OF ORDERED DECISION TREES

2002 ◽  
Vol 13 (03) ◽  
pp. 445-458 ◽  
Author(s):  
HANS ZANTEMA ◽  
HANS L. BODLAENDER
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Blow Up ◽  
Inductive Inference ◽  
Hard Problem ◽  
Minimal Size ◽  
Knowledge Based ◽  
Np Hard Problem ◽  
Decision Tables

Decision tables provide a natural framework for knowledge acquisition and representation in the area of knowledge based information systems. Decision trees provide a standard method for inductive inference in the area of machine learning. In this paper we show how decision tables can be considered as ordered decision trees: decision trees satisfying an ordering restriction on the nodes. Every decision tree can be represented by an equivalent ordered decision tree, but we show that doing so may exponentially blow up sizes, even if the choice of the order is left free. Our main result states that finding an ordered decision tree of minimal size that represents the same function as a given ordered decision tree is an NP-hard problem; in earlier work we obtained a similar result for unordered decision trees.

scholarly journals PREDICTION OF THE RISK GROUP (BY ACADEMIC PERFORMANCE) AMONG FIRST COURSE STUDENTS BY USING THE DECISION TREE METHOD

2020 ◽  
pp. 121-136
Author(s):  
Natalia Nakaryakova ◽  
◽  
Sergey Rusakov ◽  
Olga Rusakova ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Computer Science ◽  
Decision Trees ◽  
Research University ◽  
Risk Group ◽  
Applied Mathematics ◽  
Binary Classification ◽  
First Trimester ◽  
Dropout Rate

Mass education in Russian universities in specialties (direction of study) related to the exact and technical sciences is characterized by a high dropout rate, starting from the first year of study. The current level of school education, the system for selecting applicants through the USE procedure, in many cases does not guarantee that future students will be able to successfully master science-intensive specialties. An emphasis on student-centered, individual learning is possible only after students have proven themselves in the early stages of their studies. Therefore, the anticipatory identification of the ability of yesterday's applicants to study effectively is a very urgent task. In this paper, we consider methods for constructing decision trees designed to classify students, highlighting from them a lot of those (risk group) who, with a high degree of probability, will be expelled after the first academic cycle (trimester). At the same time, the minimum information about the freshmen, recorded in their personal file, is used as input data. The construction of the model was carried out according to the data on students of the applied mathematics and computer science direction of the Perm State National Research University for a five-year period of sets of 2014-2018. At the same time, the information from 2014-2017 was used for training, and the flow of 2018 was used as a test one. At the stage of machine learning, several models of decision trees were considered, which were optimized using balancing, restrictions on the maximum tree depth and the minimum number of elements in a leaf. The effectiveness of the binary classification was assessed using a matrix of inaccuracies and a number of numerical criteria obtained on its basis. As a result of machine learning, a decision tree was built, which predicted 16 out of 17 people expelled from the first trimester into the risk group. That is, for a number of reasons, they turned out to be incapable of learning in the direction of applied mathematics and computer science. In addition, it was possible to determine the level of significance of various types of initial data, showing that the results of the USE largely determine the success of students at this stage of training. The definition of the risk group provides certain guidelines for the purposeful activity of teachers and university psychologists, which ultimately can serve as a basis for improving the quality of education and reducing dropout rates. The work performed demonstrates the capabilities of data mining methods in solving poorly formalized tasks characteristic of this type of human activity.

Programming Drills with a Decision Trees Workbench

2008 ◽  
pp. 108-120
Author(s):  
Dimitris Kalles ◽  
Athanasios Pagagelis
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Computer Science ◽  
Decision Trees ◽  
Algorithm Design ◽  
Science Students ◽  
Learning Paradigm ◽  
Tree Construction ◽  
Expert User ◽  
Tree Algorithms

Decision trees are one of the most successful Machine Learning paradigms. This paper presents a library of decision tree algorithms in Java that was eventually used as a programming laboratory workbench. The initial design focus was, as regards the non-expert user, to conduct experiments with decision trees using components and visual tools that facilitate tree construction and manipulation and as regards the expert user, to be able to focus on algorithm design and comparison with few implementation details. The system has been built over a number of years and over various development contexts and has been successfully used as a workbench in a programming laboratory for junior computer science students. The underlying philosophy was to achieve a solid introduction to object-oriented concepts and practices based on a fundamental machine learning paradigm.

scholarly journals Learning Optimal Decision Trees with SAT

2018 ◽  
Author(s):  
Nina Narodytska ◽  
Alexey Ignatiev ◽  
Filipe Pereira ◽  
Joao Marques-Silva
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Practical Interest ◽  
Training Data ◽  
Fundamental Importance ◽  
Optimal Decision ◽  
Past Work ◽  
Computational Problem ◽  
Natural Mapping

Explanations of machine learning (ML) predictions are of fundamental importance in different settings. Moreover, explanations should be succinct, to enable easy understanding by humans.  Decision trees represent an often used approach for developing explainable ML models, motivated by the natural mapping between decision tree paths and rules. Clearly, smaller trees correlate well with smaller rules, and so one  challenge is to devise solutions for computing smallest size decision trees given training data. Although simple to formulate, the computation of smallest size decision trees turns out to be an extremely challenging computational problem, for which no practical solutions are known. This paper develops a SAT-based model for computing smallest-size decision trees given training data. In sharp contrast with past work, the proposed SAT model is shown to scale for publicly available datasets of practical interest.

scholarly journals Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Amir Ahmad ◽  
Ourooj Safi ◽  
Sharaf Malebary ◽  
Sami Alesawi ◽  
Entisar Alkayal
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Polymerase Chain Reaction Test ◽  
Imbalanced Datasets ◽  
Polymerase Chain ◽  
Chain Reaction Test ◽  
Selection Of

The coronavirus disease 2019 (Covid-19) pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms were applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.

scholarly journals Crop Selection and it’s Yield Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 4126-4128
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Crop Selection ◽  
Proposed Model ◽  
Approximate Percentage ◽  
Selection Of

The field of Agriculture plays a major role in the Indian economy. This sector helps to meet the basic needs of human and their civilization. Hence agriculture would be the enterprise in the globe. Considering the parameters of the agriculture, selection of crops plays a very vital role in farming. The proposed model for Crop selection and it’s yield prediction mainly focusses on the season and location to display the desired crop for cultivation . This requirement is implemented with Machine Learning algorithms like Decision tree for classification and Linear regression for yield prediction to maximize the crop yield. This model helps the farmers to know about the correct crop to be cultivated in a particular location . And also gives a approximate percentage of yield based on the data available in the dataset. Thus selecting crop for cultivation becomes a easier task for farmers because selection of correct crop for their location is precisely implemented using this project.

Impact of the WampServer application in Blended learning considering data science, machine learning, and neural networks

2020 ◽  
Vol 17 (3) ◽  
pp. 199-217 ◽  
Author(s):  
Ricardo-Adán Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Blended Learning ◽  
Learning Process ◽  
Data Science ◽  
Educational Process ◽  
School Activities ◽  
Web Programming ◽  
Teaching Learning ◽  
The Impact

This quantitative research aims to analyze the impact of the WampServer application in Blended learning during the educational process of computing through data science, machine learning, and neural networks. WampServer is a free application that allows the creation of websites considering the use of the database. This research proposes the use of Blended learning in the Development of applications subject in order to facilitate the teaching–learning process in the Database unit. The students discuss and reflect the concepts on the database in the classroom and carry out various school activities on the construction of websites at home through the use of the WampServer application. The sample consists of 28 students who took the Development of applications subject during the 2016 school year. The results of machine learning (linear regression) with 50, 60, and 70% of training indicate that the use of PHP, HTML, and SQL languages in the WampServer application positively influences the assimilation of knowledge and development of skills on web programming. Data science identifies six predictive models about the use of WampServer in the educational process of computing. On the other hand, neural networks determine the factors that influence the assimilation of knowledge and development of skills on web programming. This research recommends the incorporation of the WampServer application in the school activities related to the computer field to create new educational spaces and facilitate the teaching–learning process. Teachers can transform the educational context through the organization and realization of creative activities inside and outside the classroom. Finally, the use of WampServer in Blended learning allows creating new spaces for teaching and learning of computer science because this application favors the assimilation of knowledge and development of skills on web programming.

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