This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels. The method is an application of the aggregation method as recently proposed in the literature on theoretical machine learning, and excels in its computational convenience and capability to deal with high-dimensional data. This paper gives a formal analysis of the method, yielding rates of convergence similar to what traditional techniques obtain, while it is shown to cope well with an exponentially large set of features. Those results are supported by numerical simulations on a range of publicly available survival-micro-array data sets. It is empirically found that the proposed technique combined with a recently proposed pre-processing technique gives excellent performances. All used software files and data sets are available on the authors’ website http://user.it.uu.se/~liuya610/index.html.
|Name||Computing and Combinatorics|
|Conference||25th International Computing and Combinatorics Conference|
|Abbreviated title||COCOON 2019|
|Period||29/07/19 → 31/07/19|