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首页> 外文会议>2016 International Conference on Microelectronics, Computing and Communications >A framework of multivariant statistical model based tool using particle swarm optimization with fuzzy data for the classification of yeast data
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A framework of multivariant statistical model based tool using particle swarm optimization with fuzzy data for the classification of yeast data

机译：一个基于多变量统计模型的工具框架，使用带有模糊数据的粒子群算法对酵母数据进行分类

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摘要

Yeast is one of the major components for the formation of different medicines and various chemical products. So, yeast data classification is one of the major bioinformatics task. If the type of yeast can be categorized at primary stages based on initial characteristics of it, a lot of technical procedure can be avoided in the preparation of chemical and medical products. In this paper, an effort has been taken to classify yeast data. The yeast dataset, obtained from UCI machine learning laboratories, is used. Here, 50 selected data samples have been chosen for case study. At first Total effect of each selected samples has been calculated with Factor analysis (FA) and principal component analysis (PCA). On the basis of average error of FA and PCA, Total Effect obtained from factor analysis has been chosen for applying two soft computing models, they are Fuzzy Time Series model (FTS), and Particle Swarm Optimization model respectively. The performance of these two soft computing models is then evaluated using residual analysis.
机译：酵母是形成不同药物和各种化学产品的主要成分之一。因此，酵母数据分类是主要的生物信息学任务之一。如果酵母的类型可以根据其初始特征在初级阶段进行分类，则在化学和医疗产品的制备中可以避免很多技术步骤。在本文中，已努力对酵母数据进行分类。使用从UCI机器学习实验室获得的酵母数据集。在这里，已选择了50个选定的数据样本进行案例研究。首先，已通过因子分析（FA）和主成分分析（PCA）计算了每个选定样本的总效应。基于FA和PCA的平均误差，选择了因子分析获得的总效应来应用两个软计算模型，分别是模糊时间序列模型（FTS）和粒子群优化模型。然后，使用残差分析评估这两个软计算模型的性能。

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