Decision Trees With Hypotheses (Synthesis Lectu...
In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute.
Decision Trees with Hypotheses (Synthesis Lectu...
Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the basis for graduate courses.
In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute.
Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the basis for graduate courses.
Some sensitivity analyses can be pre-specified in the study protocol, but many issues suitable for sensitivity analysis are only identified during the review process where the individual peculiarities of the studies under investigation are identified. When sensitivity analyses show that the overall result and conclusions are not affected by the different decisions that could be made during the review process, the results of the review can be regarded with a higher degree of certainty. Where sensitivity analyses identify particular decisions or missing information that greatly influence the findings of the review, greater resources can be deployed to try and resolve uncertainties and obtain extra information, possibly through contacting trial authors and obtaining individual participant data. If this cannot be achieved, the results must be interpreted with an appropriate degree of caution. Such findings may generate proposals for further investigations and future research.
Order-optimized sequential trees can therefore be good methods for the synthesis of clinical trial data. Other methods, such as deep learning models that are also being applied to the synthesis of health data, do not scale down well to the small datasets typically encountered with clinical trials.
Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with an entire methodology. The world at-large is confronted with increasingly larger and complex sets of structured/unstructured information; from sensors, instruments, and generated by computer simulations; data is "hidden" in websites, application servers, social networks and on mobile devices. As a nation, assimilating information across disparate domains (e.g., intelligence, economics, science) has the potential to provide improved capabilities for decision makers. In commerce and industry, analytics-driven enterprises are becoming mainstream. Yet, there is a shortfall in the key education skills needed to meet the growing needs. Traditional enterprises are moving toward analytics-driven approaches for core business functions. In the government and corporations, cybersecurity problems are prevalent. The investment in advanced analytics capabilities could potentially be more broadly leveraged today and greater than any prior government investments in computing. Emphasis is now placed on disruptive data and information sources on the Web and Internet: using Web Science and informatics to explore social networks, platform competition, the "long tail" and economic or resource impacts of the search for new findings. Key topics include: advanced statistical computing theory, multivariate analysis, and application of computer science courses such as data mining and machine learning and change detection by uncovering unexpected patterns in data.
EECS 545. Machine Learning (CSE)Advisory Prerequisite: Coursework in probability, linear algebra, and programming. (Credit Exclusions: No credit to a student who has taken EECS 445, 453, or 553) (3 credits)Fundamentals of supervised, unsupervised, and sequential learning, including linear and nonlinear regression, logistic regression, support vector machines and kernel methods, decision trees, ensemble methods, neural networks and deep learning, dimension reduction, clustering, and probabilistic models. Emphasis on implementation and application to real-world data. Includes algorithms and derivations from fundamental principles. CourseProfile (ATLAS)
EECS 553. Machine Learning (ECE) Advisory Prerequisite: Graduate coursework in probability and linear algebra. (3 credits) (Students may not receive credit for both EECS 553 and EECS 545)Fundamentals of supervised, unsupervised, and sequential learning, including linear and nonlinear regression, logistic regression, support vector machines and kernel methods, decision trees, ensemble methods, neural networks and deep learning, dimension reduction, clustering, and probabilistic models. Emphasis on algorithms and their derivation from fundamental principles, includes applications to real-world data. Projects are overseen/graded by faculty and may also involve mentoring by representatives from external organizations. CourseProfile (ATLAS)
Z. Yang et al. (2015)788 found that future climate scenarios for the USA (A1B 1.6C and B1 2C in the 2040s) had a greater effect on salinity intrusion than future land-use/land-cover change in the Snohomish River estuary in Washington state (USA). This resulted in a shift in the salinity both upstream and downstream in low flow conditions. Projecting impacts in deltas needs an understanding of both fluvial discharge and SLR, making projections complex because the drivers operate on different temporal and spatial scales (Zaman et al., 2017; Brown et al., 2018b)789. The mean annual flood depth when 1.5C is first projected to be reached in the Ganges-Brahmaputra delta may be less than the most extreme annual flood depth seen today, taking into account SLR, surges, tides, bathymetry and local river flows (Brown et al., 2018b)790. Further, increased river salinity and saline intrusion in the Ganges-Brahmaputra-Meghna is likely with 2C of warming (Zaman et al., 2017)791. Salinization could impact agriculture and food security (Cross-Chapter Box 6 in this chapter). For 1.5C or 2C stabilization conditions in 2200 or 2300 plus surges, a minimum of 44% of the Bangladeshi Ganges-Brahmaputra, Indian Bengal, Indian Mahanadi and Ghanese Volta delta land area (without defences) would be exposed unless sedimentation occurs (Brown et al., 2018b)792. Other deltas are similarly vulnerable. SLR is only one factor affecting deltas, and assessment of numerous geophysical and anthropogenic drivers of geomorphic change is important (Tessler et al., 2018)793. For example, dike building to reduce flooding and dam building (Gupta et al., 2012)794 restricts sediment movement and deposition, leading to enhanced subsidence, which can occur at a greater rate than SLR (Auerbach et al., 2015; Takagi et al., 2016)795. Although dikes remain essential for reducing flood risk today, promoting sedimentation is an advisable strategy (Brown et al., 2018b)796 which may involve nature-based solutions. Transformative decisions regarding the extent of sediment restrictive infrastructure may need to be considered over centennial scales (Brown et al., 2018b)797. Thus, in a 1.5C or 2C warmer world, deltas, which are home to millions of people, are expected to be highly threatened from SLR and localized subsidence (high confidence). 041b061a72