APTER: Aggregated Prognosis Through Exponential Reweighting
This is an incremental improvement for medical prognosis using existing machine learning techniques on new data.
The paper tackles the problem of predicting patient prognosis from high-dimensional micro-array data by applying an aggregation method, achieving excellent performance in simulations on public datasets.
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. A formal analysis of the method is given, 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 datasets. It is empirically found that the proposed technique combined with a recently proposed preprocessing technique gives excellent performances.