期刊名称：Journal of Computer Science & Systems Biology
印刷版ISSN：0974-7230
出版年度：2013
卷号：6
期号：5
页码：291-297
DOI：10.4172/jcsb.1000124
出版社：OMICS Publishing Group
摘要：Microarrays have already produced huge amounts of valuable genetic data that is challenging to analyse due to its high dimensionality and complexity. An inherent problem with the microarray data which is characteristic of diseases such as Alzheimer’s is that they face computational complexity due to the sparseness of the points within the data, which affect both the accuracy and the efficiency of supervised learning methods. This paper proposes a data-adaptive rule-based classification system for Alzheimer’s disease classification that generates relevant rules by finding adaptive partitions using gradient-based partitioning of the data. The adaptive partitions are generated from the histogram by analyzing Tuple Tests following which efficient and relevant rules are discovered that assist in classifying the new data correctly. The proposed approach has been compared with other rule-based and machine learning classifiers, and detailed results and discussion of the experiments are presented to demonstrate comparative analysis and the efficacy of the results.
关键词：Alzheimer’s; Partitioning; Classification; Rule tuple test
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