Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 207 for “"decision trees."”.
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Efficiently Learning Monotone Decision Trees with ID3
… findings show that ID3 will produce an optimal decision tree for this class of Boolean functions.</p>
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Conversion of Decision Tables Into Decision Trees
Made available in DSpace on 2014-12-10T20:13:33Z (GMT). No. of bitstreams: 1 7219965.pdf: 3392556 bytes, checksum: 31971815fd9556aa918a03c2db282293 (MD5) Previous issue date: 1972
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Naturally Generated Decision Trees for Image Classification
… we have little to no ability to understand the decision process taken by a network to reach a conclusion. This factor poses a difficulty in use cases such as medical diagnostics tools or autonomous vehicles, which require insight into prediction reasoning to validate a conclusion or to debug a …
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Ontology-based annotation using naive Bayes and decision trees
… defined by the ontology using a probabilistic decision tree model. Our solution outperforms conventional text mining approaches by taking advantage of an ontology. We consider five essential ontological components (Stimulus Modality, Stimulus Type, Response Modality, Response Type, and …
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Explaining Black-Box Classifiers by Implicitly Learning Decision Trees
… connection to the problem of implicitly learning decision trees. The implicit nature of this learning task allows for efficient algorithms even when the complexity of f necessitates an intractably large surrogate decision tree. We solve the implicit learning task by bringing together techniques …
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Budget allocation on differentially private decision trees and random forests
… & Sarwate, 2011; Friedman & Schuster, 2010). Decision tree induction is a textbook case for such a problem. The algorithmic decisions in trees that are made with privacy considerations, have a deep impact on the accuracy of the results. An improved privacy-preserved decision tree algorithm, …
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Variable Selection and Decision Trees: The DiVaS and ALoVaS Methods
… we propose a novel modification to Bayesian decision tree methods. We provide a historical survey of the statistics and computer science research in decision trees. Our approach facilitates covariate selection explicitly in the model, something not present in previous research. We define a …
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On the Learnability of Disjunctive Normal Form Formulas and Decision Trees
… of disjunctive normal form formulas and decision trees is investigated. Polynomial time algorithms are given, and nonlearnability results are obtained, for restricted versions of these general learning problems.
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Feature construction: An analytic framework and an application to decision trees
… system that constructs new features using decision tress. CITRE was tested on five learning problems: l-term kDNF Boolean functions, tic-tac-toe classification, mushroom classification, voting-record classification, and chess-end-game classification. The results demonstrate CITRE's …
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On Dynamic Treatment Regimes: Collaborative Search and LLM-Driven Decision Trees
… a novel data structure for extracting treatment decisions from unstructured clinical notes. The main contribution is the Clinical Decision Tree (CDT) which uses large language models (LLMs) to extract key decisions in chronic disease treatment. This addresses the main pain points in dynamic …
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Practical Implementation of a Security-Dependability Adaptive Voting Scheme Using Decision Trees
Today's electric power system is operated under increasingly stressed conditions. As electrical demand increases, the existing grid is operated closer to its stable operating limits while maintaining high reliability of electric power delivery to its customers. Protective schemes are designed to …
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Discrete and Continuous Nonconvex Optimization: Decision Trees, Valid Inequalities, and Reduced Basis Techniques
… a strategic risk management problem via a novel decision tree optimization approach, as well as development of enhanced Reformulation-Linearization Technique (RLT)-based linear programming (LP) relaxations for solving nonconvex polynomial programming problems, through the generation of valid …
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A Simulated Annealing Approach to Designing Optimal Decision Trees for Classification, Prescriptive, and Survival Analysis
A binary decision tree is a highly interpretable machine learning model, as humans can easily understand how a prediction is made by answering a series of binary questions. Earlier work has provided a powerful framework for constructing optimal decision trees by utilizing multiple random warm …
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An Efficient Ranking and Classification Method for Linear Functions, Kernel Functions, Decision Trees, and Ensemble Methods
Structural algorithms incorporate the interdependence of outputs into the prediction, the loss, or both. Frank-Wolfe optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 …
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The use of decision trees to empower production technicians to troubleshoot routine process and equipment problems
Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1996, and Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1996 .
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Analyzing biological expression data based on decision tree induction
… and exploitation of one basic technique: decision trees. The concept of comparing sets of decision trees is developed. This method offers the possibility of identifying significant thresholds in continuous or discrete valued attributes through their corresponding set of decision trees. …
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Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning
… machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64% accuracy. The data reduction techniques are …
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Brief Study of Classification Algorithms in Machine Learning
… Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an …
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Evaluation of boolean formulas with restricted inputs
… paper are the quantum walk algorithm for NAND trees given by Farhi and Gutmann [2], and an algorithm for more general boolean formulas based on span programs, given by Reichardt and Spalek [6]. I will show that these algorithms can run much faster on a certain set of inputs, and that there is a …
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