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首页> 外文期刊>Mapan: Journal of Metrology Society of India >An Intelligent Two Phase Fuzzy Decision Tree Based Clustering Model for Design of Computer Aided Detection/Diagnosis (CADe/CADx) System
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An Intelligent Two Phase Fuzzy Decision Tree Based Clustering Model for Design of Computer Aided Detection/Diagnosis (CADe/CADx) System

机译：基于智能的两相模糊决策树基于计算机辅助检测/诊断设计（CADE / CADX）系统的群集模型

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摘要

Automated computer aided detection/diagnosis (CADe/CADx) system plays a key role in decision making process and work as a recommender system for researchers. Nowadays, CADe/CADx systems are getting popular due to their strong ability to extract complex relations present in unprocessed data set. In this research article, we present an intelligent two phase classification model for the design of CADe/CADx system. In order to design an intelligent CAD system, the primary challenge lies in identifying important attributes. The presence of irrelevant and redundant attributes in the data can have adverse impact over classifier accuracy. An efficient dimensionality reduction technique aims at achieving lower computational cost with reduced storage requirement by choosing problem specific relevant or significant attributes. The secondary challenge is to provide unambiguous and comprehensible rule base for accurate predictions. The contribution of work can be stated twofold: first, to attain reasonably good classification accuracy with possible speed up, linear discriminant analysis and some popular correlation coefficients (Fisher, Phi and Point bi-serial) are being used to identify significant attributes. Second, to generate comprehensible and understandable rule set a fuzzy decision tree based clustering approach is used. The performance of proposed model is verified on twelve famous UCI data sets.
机译：自动化计算机辅助检测/诊断（CADE / CADX）系统在决策过程中起关键作用，并作为研究人员的推荐系统。如今，CADE / CADX系统由于其强烈提取了在未处理的数据集中存在的复杂关系而受欢迎。在本研究文章中，我们为CADE / CADX系统设计了一个智能的两相分类模型。为了设计智能CAD系统，主要挑战在于确定重要属性。数据中存在无关紧要和冗余属性可能对分类器精度产生不利影响。有效的维度减少技术旨在通过选择特定的相关或重大属性来实现降低的存储需求计算成本。次要挑战是为准确的预测提供明确和可理解的规则基础。工作的贡献可以说是双重的：首先，以可能的加速，线性判别分析和一些流行的相关系数（FISHER，PHI和点双序列）达到合理良好的分类准确性来识别重要属性。其次，要生成可识别和可理解的规则集，使用了基于模糊的决策树的聚类方法。在12个着名的UCI数据集中验证了所提出的模型的性能。

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