ITR-AP: A Program for Predicting and Understanding Data

摘要DMS 0112734-目前正在收集的大部分科学数据的两个主要用途是首先通过计算机算法使用数据预测未来观测对象的类型。 例如，其中一个问题是开发算法，对光学和射电望远镜拍摄的数百万个恒星物体进行分类。 第二：了解哪些变量区分不同类型的对象。 一个例子是定位区分癌性和非癌性DNA的基因活性。 该项目建立在最近发现的一种称为随机森林的算法之上，当进一步开发并与交互式图形显示相结合时，将为回答这些问题提供一种先进的工具。随机森林是一种来自机器学习背景的新预测算法，其功能是通过组合数百个随机生成的二元决策来实现。 它已经在具有数千个变量的大型数据集上展示了最先进的预测准确性。 它生成了大量关于数据而不是预测的信息。 这些信息可以用来估计变量的重要性，聚类，密度估计等，以使这些信息更容易理解的用户，交互式图形，如平行坐标和层次聚类图连接在一起将被纳入到一个程序的设计，使用一个更高度发达的随机森林算法作为底层引擎。

ABSTRACT DMS 0112734-The two main uses of much of the scientific data currently being collected are first to use the data to predict the types of future objects observed by means of computerized algorithms. For instance, one such problem is to develop algorithms that will classify the millions of stellar objects recorded on photographic images by optical and radio telescopes. Second: to understand which variables are discriminating between different types of objects. An example is in locating the gene activity that discriminates between cancerous and non cancerous DNA. This project builds on a recently discovered algorithm called random forests which, when further developed and combined with interactive graphical displays, will provide an advanced tool for answering these questions.Random forests is a new prediction algorithm coming from the Machine Learning context that functions by combining hundreds of randomly generated binary decision. It has demonstrated state-of-the-art prediction accuracy on large data sets with thousands of variables. It generates a wealth of information about the data other than the prediction. This information can be used to estimate variable importance, clustering, density estimation etc. To make this information more readily understandable to the user, interactive graphics such as parallel coordinates and hierarchical cluster diagrams linked together will be incorporated into the design of a program using a more highly developed random forests algorithm as the underlying engine.