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Adaptive Fuzzy Logic based framework for handling imprecision and uncertainty in pattern classification of bioinformatics datasets.

机译：基于自适应模糊逻辑的框架，用于处理生物信息学数据集模式分类中的不精确性和不确定性。

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摘要

Classification in the emerging field of Bioinformatics is a challenging task because the information about different diseases is either insufficient or lacking in authenticity as data is collected from different types of medical equipment. Also the limitation of human expertise in manual diagnoses leads to incorrect diagnoses. Moreover, the information gathered from various sources is subject to imprecision and uncertainty. Researchers utilized Artificial Neural Networks, Support Vector Machine and Bayesian Networks to achieve better classification, but the developed models are bedeviled by several limitations especially in uncertain situations. Recently, Type-1 and Type-2 Fuzzy Logic Systems (FLS) have been introduced as novel computational intelligence approaches for both prediction and classification. However Type-2 and other FLS have not been fully utilized in the bioinformatics and medical science. This thesis presents a Type-2 FLS-based classification framework for multivariate data to diagnose different types of diseases, which is capable of handling imprecision and uncertainty. As expected, this new computational intelligence approach overcomes the weaknesses of existing classifiers, particularly in the ability to handle data in uncertain situations such as uncertainty due to the existence of various types of noise, inconsistent expert opinions, ignorance and laziness. The classification accuracy and performance of the proposed framework are measured by using University of California, Irvine (UCI) well known medical datasets. The classification is performed on the basis of the nature of the inputs (e.g., singleton or non-singleton) and on whether uncertainty is present or absent. Empirical results have shown that the proposed FLS classification framework outperforms earlier implemented models with better classification accuracy among all existing classifiers. In addition, we conducted empirical studies on this classifier regarding the impact of various parameters of the proposed framework such as training algorithms and defuzzification methods.
机译：在新兴的生物信息学领域中，分类是一项具有挑战性的任务，因为随着从不同类型的医疗设备收集数据，有关不同疾病的信息要么不足，要么缺乏真实性。此外，人工诊断方面的专业知识的局限也会导致错误的诊断。此外，从各种来源收集的信息可能不精确且不确定。研究人员利用人工神经网络，支持向量机和贝叶斯网络来实现更好的分类，但是开发的模型受到一些限制，特别是在不确定的情况下。最近，已经将类型1和类型2模糊逻辑系统（FLS）引入作为用于预测和分类的新型计算智能方法。但是，Type-2和其他FLS尚未在生物信息学和医学中得到充分利用。本文提出了一种基于类型2 FLS的多变量数据分类框架，用于诊断不同类型的疾病，能够处理不精确性和不确定性。如预期的那样，这种新的计算智能方法克服了现有分类器的弱点，特别是在不确定情况下处理数据的能力，例如由于各种噪声的存在，专家意见不一致，无知和懒惰而导致的不确定性。通过使用加利福尼亚大学欧文分校（UCI）著名的医学数据集来衡量所提出框架的分类准确性和性能。根据输入的性质（例如，单例或非单例）以及是否存在不确定性来进行分类。实证结果表明，在所有现有分类器中，所提出的FLS分类框架均优于早期实施的模型，且分类精度更高。此外，我们对该分类器进行了实证研究，研究了所提出框架的各种参数（例如训练算法和解模糊方法）的影响。

著录项

  • 作者

    Rasheed, Zeehasham.;

  • 作者单位

    King Fahd University of Petroleum and Minerals (Saudi Arabia).;

  • 授予单位 King Fahd University of Petroleum and Minerals (Saudi Arabia).;
  • 学科 Computer Science.
  • 学位 M.S.
  • 年度 2009
  • 页码 109 p.
  • 总页数 109
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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