Data mining in bioinformatics using Weka
Data mining in bioinformatics using Weka
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DOI：
10.1093/bioinformatics/bth261
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发表时间：
2004-10-12
期刊：
影响因子：
5.8
通讯作者：
Witten, IH
中科院分区：
文献类型：
--
作者：
Frank, E;Hall, M;Witten, IH
Weka机器学习工作台为自动分类、回归、聚类和特征选择提供了一个通用的环境，这些都是生物信息学研究中常见的数据挖掘问题。它包含了广泛的机器学习算法和数据预处理方法的集合，辅以用于数据探索的图形用户界面和针对同一问题的不同机器学习技术的实验比较。Weka可以处理以单个关系表形式给出的数据。它的主要目标是(a)协助用户从数据中提取有用的信息，以及(b)使他们能够轻松地识别合适的算法，以便从中生成准确的预测模型。
The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it.