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Title:
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A NEW HYBRID MODEL USING CASE-BASED REASONING AND DECISION TREE METHODS FOR IMPROVING SPEEDUP AND ACCURACY |
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Author(s):
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Asgarali Bouyer , Bahman Arasteh , Ali Movaghar |
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ISBN:
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978-972-8924-30-0 |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Case-Based Reasoning, Decision Tree, Data mining, Classification. |
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Type:
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Poster/Demonstration |
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First Page:
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787 |
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Last Page:
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789 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Case-Based Reasoning (CBR) is one of the preferred problem-solving strategies and machine learning techniques in complex and dynamically changing situations [1]. In addition, Decision Tree is one of the most popular and frequently used methods in data mining for searching predictive information. In this paper, we present a new hybrid method using Case-Based Reasoning and Decision Tree to construct an efficiently new approach over centralized data. Then we tried to compare our method with a standard CBR technique implementation in terms of accuracy and speedup. Our experimental results show that by using CBR on our desired sample related to its own class in Decision Tree, we will get a better speedup with reduced error rate (high confidence). It has been widely adopted in a high performance field to deal with huge data and complex computation. The method is able to deal with large data sets while using a sequential implementation of Decision Tree. |
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