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Optimize Algorithm of Decision Tree Based on Rough Sets Hierarchical Attributes
Abstract
Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. And now it has been widely applied in constructing decision tree which has no hierarchical attributes inside. However, hierarchical attributes exist generally in realistic environment, which leads that decision making has max rules. Using max rules to build decision trees can optimize decision trees and has practical values as well. So, in order to deal with hierarchical attributes in decision tree, this paper try to design an optimize algorithm of decision tree based on rough sets hierarchical attributes (ARSHA), which works by combining the hierarchical attribute values and deleting the associated objects when max rules exist in decision table. So that the algorithm developed in this paper can abstract the simplest rule set that can cover all information for decision making. Finally, a real example is used to demonstrate its feasibility and efficiency.
... There are several algorithms developed for optimization problems. [25][26][27][28] In some particular cases, an analytical methodology can be followed although due to the usual complexity of the optimization problems, most of the methods are based on heuristic and/or iterative approaches. In this work, given its speci¯c formulation, an iterative approach is followed, where the thresholds of the DTs are iteratively Integration of Current Clinical Knowledge with a Data Driven Approach 5 optimized based on dependencies among risk factors. ...
Cardiovascular diseases are the leading cause of death worldwide. The development of models to support clinical decision is of great importance in the management of these diseases. This work aims to improve the performance exhibited by risk assessment scores that are applied in the clinical practice. This methodology has three main phases: (i) representation of scores as a decision tree; (ii) optimization of the decision tree thresholds using data from recent clinical datasets; (iii) transformation of the optimized decision tree into a new score.
This approach was validated in a cardiovascular disease secondary prevention context, supported by a dataset provided by the Portuguese Society of Cardiology ([Formula: see text]). The respective performance was assessed using statistical metrics and was compared with GRACE score, the reference in Portuguese clinical practice. The new model originated a better balance between the sensitivity and specificity when compared with the GRACE, originating an accuracy improvement of approximately 22%.
The Cardiovascular Diseases (CVD) are the leading cause of death in the world, being prevention recognized to be a key intervention able to contradict this reality. In this context, although there are several models and scores currently used in clinical practice to assess the risk of a new cardiovascular event, they present some limitations. The goal of this paper is to improve the CVD risk prediction taking into account the current models as well as information extracted from real and recent datasets. This approach is based on a decision tree scheme in order to assure the clinical interpretability of the model. An innovative optimization strategy is developed in order to adjust the decision tree thresholds (rule structure is fixed) based on recent clinical datasets. A real dataset collected in the ambit of the National Registry on Acute Coronary Syndromes, Portuguese Society of Cardiology is applied to validate this work. In order to assess the performance of the new approach, the metrics sensitivity, specificity and accuracy are used. This new approach achieves sensitivity, a specificity and an accuracy values of, 80.52%, 74.19% and 77.27% respectively, which represents an improvement of about 26% in relation to the accuracy of the original score.
We investigate in this paper approximate operations on sets, approximate equality of sets, and approximate inclusion of sets. The presented approach may be considered as an alternative to fuzzy sets theory and tolerance theory. Some applications are outlined.
An efficient algorithm for reduction of attribute in information systems is put forward, whose time complexity is max{O((|C|+|D|) |U|),O(|C|2|U|))}. Compared with those existing heuristic algorithms, its flexibility has been increased because calculating the important value of unimportant attribute repeatedly can be avoided by removing unimportant attributes gradually from the search space. The theoretical analysis and experimental results show that this algorithm is much less time complexity than those existed algorithms; and a real example demonstrates its feasibility and effectiveness, respectively.
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity and are therefore incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees while retaining their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains.
Attribute reduction is one of the key problems in Rough Set theory, and many algorithms have been proposed for static data. Very little work has been done in incremental attribute reduction algorithm. In this paper, an incremental attribute reduction algorithm is present. In order to reduce the computational complexity, a fast counting sort algorithm is introduced for dealing with redundant and inconsistent data in decision tables. When the objects in decision table increase dynamically, a new reduct can be updated by the old reduct effectively. Experiments show that our algorithm outperforms other incremental attribute reduction algorithms.
Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. Recently, the rough-set theory has been widely used in dealing with data classification problems. Most of the previous studies on rough sets focused on deriving certain rules and possible rules on the single concept level. Data with hierarchical attribute values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new learning algorithm based on rough sets to find cross-level certain and possible rules from training data with hierarchical attribute values. It is more complex than learning rules from training examples with single-level values, but may derive more general knowledge from data. Boundary approximations, instead of upper approximations, are used to find possible rules, thus reducing some subsumption checking. Some pruning heuristics are also adopted in the proposed algorithm to avoid unnecessary search.
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This paper puts forward a new method of discretizing continuous attributes. Compared with the traditional approach, the method, proposed in this paper, can make the number of the obtained classes be more moderate, as well as the lost information be fewer. And then a simple attribute reduction algorithm is developed in continuous information systems. Finally, a real example is used to illustrate its feasibility and effectiveness, respectively.
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- Wang Yan-Bing
- Zhao Rui
- Yao
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- Miao Duo-Qian
- Wang
MIAO Duo-qian, WANG Jue. Method for constructing Multi-attributes decision tree based on rough sets,Jour-nal of software,1997,8(6):425-431.
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- Huang
- Ding-Xuan
- Wu
HUANG Ding-xuan, WU Zhen-ye. Approach for Weighed Continuous Multivariate Decision Tree, System Engineering-Theory Methodology Applications, 2005,14(1):80-83.
New method for constructing decision tree based on rough set theory, ComputerApplications
- Jiang
- Yun
- Li Zhan-Huai
- Zhang
- Qiang
- Liu
- Yang
JIANG Yun, LI Zhan-huai, ZHANG Qiang, LIU Yang. New method for constructing decision tree based on rough set theory, ComputerApplications,2004,24(8):21-23.
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- Jue
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- Li Zhan-Huai
- Liu Zhang Qiang
- Yang