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Java库,用于生成和使用生成的决策树

[英]Java library to generate and work with generated decision trees

I'm looking for a Java libraries that can not only build decision trees using ID3 or C4.5 algorithms, but also store newly built tree in some suitable format. 我正在寻找一个Java库,它不仅可以使用ID3或C4.5算法构建决策树,而且可以以某种合适的格式存储新构建的树。 The matter is that I'am planning to use a decision trees engine as a some kind of user needs deduction system, ie after generating decision tree using training data I would like to give it the input (data from user) and to use output as a recomendation to user. 问题是我打算将决策树引擎用作某种类型的用户需求推导系统,即,在使用训练数据生成决策树之后,我想为其提供输入(来自用户的数据)并将输出用作给用户的推荐。 Simply saying, I just wan't to be able to walk through the generated tree and get the result according to input data set. 简而言之,我只是无法浏览生成的树并根据输入数据集获取结果。 I also want to build not only binary decision trees, but trees with changeable amount of children for each parent node(this means that some nodes may have two children, some three, all according to the values of attribute checked in the parent node). 我还不仅要构建二进制决策树,还要构建每个父节点具有可变数量的子代的树(这意味着某些节点可能有两个子代,其中三个根据父节点中检查的属性值)。 I just have started working with decision trees and don't have much experience in this field. 我刚刚开始使用决策树,在该领域没有太多经验。 I searched using google, started looking at WEKA, but I am not shure if it satisfies my requirments. 我使用Google搜索,开始查看WEKA,但是我不确定它是否满足我的要求。 Any guidance would be a great help. 任何指导都会有很大帮助。 Thank's in advance! 提前致谢!

Weka is the state of the art tool for classification, including C4.5. Weka是先进的分类工具,包括C4.5。 Defintitely give it a try. 一定要试试看。

In fact, many other tools (KNIME, Rapidminer) will often just wrap around Weka, essentially doing some data preprocessing, user interface, experiment runs etc., but the main task is delegated to Weka. 实际上,许多其他工具(KNIME,Rapidminer)通常只会包裹Weka,本质上是做一些数据预处理,用户界面,实验运行等工作,但是主要任务委托给Weka。

In my opinion Weka isn't perfectly designed for this, but it is the reference implementation when it comes to classification and prediction. 在我看来,Weka并不是为此而设计的,但是它是分类和预测时的参考实现。 So give it a try! 所以试试吧! When you are looking into other tasks such as clustering or anomaly detection, go with other tools such as ELKI instead. 当您在研究其他任务(例如聚类或异常检测)时,请使用其他工具(例如ELKI)。 Wekas clustering algorithms are minimal. Wekas聚类算法极少。

与weka一起使用,它非常流行,高效,稳定,有据可查,人们将能够为您提供帮助。

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