Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
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TL;DR: Hockey was chosen as an example to illustrate the potential use of decision tree inductions for the purpose of identifying and communicating characteristics that drive the outcome and the suitability of decision trees for analysing the features of one-versus-one exchanges are discussed.
Abstract: Decision tree induction is a novel approach to exploring attacker-defender interactions in many sports. In this study hockey was chosen as an example to illustrate the potential use of decision tree inductions for the purpose of identifying and communicating characteristics that drive the outcome. Elite female players performed one-versus-one contests (n = 75) over two sessions. Each contest outcome was classified as either a win or loss. Position data were acquired using radio-tracking devices, and movement-based derivatives were calculated for two time epochs (5 to 2.5 seconds, and 2.5 to zero seconds before the outcome occurred). A decision tree model was trained using these attributes from the first session data, which predicted that when the attacker was moving at ≥ 0.5 m · s−1 faster than the defender during the early epoch, the probability of an attacker's win was 1.00. Conversely, when the speed difference at that time was below this threshold the probability of a loss was 0.78. Secondary...
33 citations
31 Jan 2011
TL;DR: The subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains and will reach a broad market both within academia and industry.
Abstract: Mining of Data with Complex Structures:- Clarifies the type and nature of data with complex structure including sequences, trees and graphs- Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining.-Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints.- Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.)- Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees.- Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees.- Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach.- Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies.- Details the extension of the TMG framework for sequence mining- Provides an overview of the future research direction with respect to technical extensions and application areasThe primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains.
32 citations
TL;DR: The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study, and the boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods.
32 citations
Journal Article•
TL;DR: In this paper, two new methodologies for the design of efficient secure protocols, that differ with respect to their underlying computational models, are proposed, which are more efficient than previously known ones in either communication or computation.
Abstract: We suggest two new methodologies for the design of efficient secure protocols, that differ with respect to their underlying computational models. In one methodology we utilize the communication complexity tree (or branching for f and transform it into a secure protocol. In other words, "any function f that can be computed using communication complexity c can be can be computed securely using communication complexity that is polynomial in c and a security parameter". The second methodology uses the circuit computing f, enhanced with look-up tables as its underlying computational model. It is possible to simulate any RAM machine in this model with polylogarithmic blowup. Hence it is possible to start with a computation of f on a RAM machine and transform it into a secure protocol.
We show many applications of these new methodologies resulting in protocols efficient either in communication or in computation. In particular, we exemplify a protocol for the "millionaires problem", where two participants want to compare their values but reveal no other information. Our protocol is more efficient than previously known ones in either communication or computation.
32 citations
TL;DR: It is suggested that the proposed method which comprises of decision tree and DWT techniques with sound signals can be recommended for the applications of fault diagnosis of the face milling tool.
32 citations