Neural network and rough set hybrid scheme for prediction of missing associations

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-11-01 DOI:10.1504/IJBRA.2015.073237

A. Anitha, D. Acharjya

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引用次数: 20

Abstract

Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.

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缺失关联预测的神经网络与粗糙集混合方案

目前，互联网是分布式计算的最佳工具，分布式计算涉及到数据的地理分布。但是，从庞大的数据中检索信息是至关重要的，除非它提供了某些信息，否则没有任何相关性。缺失关联的预测可以被视为机器学习中的基本问题，其主要目标是确定缺失关联的决策。为此发展了朴素贝叶斯结构、人组成网络结构、贝叶斯网络建模等数学模型。但是，它有一定的局限性，没有考虑到不确定性。因此，引入粗糙集理论对数据的不一致性进行了处理。本文采用预处理和后处理两个过程来预测属性值中缺失关联的决策。预处理中使用粗糙集进行降维，后处理中使用神经网络对缺失关联进行决策。一个现实生活中的例子被提供，以显示可行性提出的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.