Abstract
This paper describes a method that can be seen as an improvement of, the standard progressive sampling. The standard method uses samples of data of increasing size until accuracy of the learned concept cannot be further improved. The issue we have addressed here is how to avoid using some of the samples in this progression. The paper presents a method for predicting the stopping point using a meta-learning approach. The method requires just four iterations of the progressive sampling. The information gathered is used to identify the nearest learning curves, for which the sampling procedure was carried out fully. This in turn permits to generate the prediction regards the stopping point. Experimental evaluation shows that the method can lead to significant savings of time without significant losses of accuracy.

Abstract

Abstract

Abstract

Abstract

Abstract
IndLog is a general purpose Prolog-based Inductive Logic Programming (ILP) system. It is theoretically based on the Mode Directed Inverse Entailment and has several distinguishing features that makes it adequate for a wide range of applications. To search efficiently through large hypothesis spaces, IndLog uses original features like lazy evaluation of examples and Language Level Search. IndLog is applicable in numerical domains using the lazy evaluation of literals technique and Model Validation and Model Selection statistical-based techniques. IndLog has a MPI/LAM interface that enables its use in parallel or distributed environments, essential for Multi-relational Data Mining applications. Parallelism may be used in three flavours: splitting of the data among the computation nodes; parallelising the search through the hypothesis space and; using the different computation nodes to do theory-level search. IndLog has been applied successfully to major ILP literature datasets from the Life Sciences, Engineering, Reverse Engineering, Economics, Time-Series modelling to name a few.