Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems

Decision trees and decision rule systems play important roles as classifiers, knowledge representation tools, and algorithms. They are easily interpretable models for data analysis, making them widely used and studied in computer science. Understanding the relationships between these two models is an important task in this field. There are well-known methods for converting decision trees into systems of decision rules. In this paper, we consider the inverse transformation problem, which is not so simple. Instead of constructing an entire decision tree, our study focuses on a greedy polynomial time algorithm that simulates the operation of a decision tree on a given tuple of attribute values.Comment: arXiv admin note: substantial text overlap with arXiv:2305.01721, arXiv:2302.0706

Bounds on Depth of Decision Trees Derived from Decision Rule Systems

Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse transformation is a more difficult task. In this paper, we study unimprovable upper and lower bounds on the minimum depth of decision trees derived from decision rule systems depending on the various parameters of these systems

An Exploration Study of using the Universities Performance and Enrolments Features for Predicting the International Quality

Quality ranking systems are crucial in the assessment of the academic performance of an institution because these assessment systems give details about how different learning institutions deliver their services. Education quality is also of paramount importance to the students because it is through quality education that these students develop skills that are needed in the job market. Besides, education enhances a student\u27s academic and reasoning capacities. When universities are subjected to ranking systems, they are likely to improve their quality to be ranked high in the system. When the university administrators are exposed to ranking, competition gears up. Through competition, the quality of education also improves and through that the general education system improves. In addition, with rapid technological progress, increased human mobility and economic growth, the concept of quality assessment at the national level has shifted to an international level and now the evaluation of higher education quality is being conducted on the basis of international standards and comparisons. In the present context, a global ranking of a university has a significant influence on attracting research funding and academic talent. Universities are expected to collaborate and compete on an international level, and it is no longer enough to achieve excellence within any national group. It is therefore, not surprising that there is a rising tendency among universities to become centres of World class excellence . The findings of this study indicated that teaching, citations, income, number of students are key predictors for predicting the international outlook of universities. Also, it showed that geography is a significant contributor that recognized when it was added to the models for assessing the quality of the worldwide universities

Regulatory Data Science for Medical Devices

Regulations that cover the legal obligations that manufacturers are bound to are essential for keeping the general public safe. Companies need to follow the regulations in order to bring their products to market. A good understanding of the regulations and the regulatory pathway defines how fast and at what cost the manufacturer can introduce innovations to the market. Regulatory technology and data science can lead to new regulatory processes and evidence in the medical field. It can equip stakeholders with unique tools that can make regulatory decisions more objective, efficient, and accurate. This book describes the latest research within the broader domain of Medical Regulatory Technology (MedRegTech). It covers concepts such as the complexity and user-friendliness of medical device regulations, novel algorithms for regulatory navigation, descriptive datasets from a health service provider, regulatory data science techniques, and considerations of the environmental impacts within a national health service. This book brings all these aspects together to offer an introduction into MedRegTech research. In the long term, these technologies and methods will help optimize the regulatory strategy for individual healthcare innovations and revolutionize the way we engage with regulatory services