M. José Ramírez-Quintana

Incremental Learning of Functional Logic Programs

In this work, we consider the extension of the Inductive Functional Logic Programming (IFLP) framework in order to learn functions in an incremental way. In general, incremental learning is necessary when the number of examples is infinite, very large or presented one by one. We have performed this extension in the FLIP system, an implementation of the IFLP framework. Several examples of programs which have been induced indicate that our extension pays off in practice. An experimental study of some parameters which affect this efficiency is performed and some applications for programming practice are illustrated, especially small classification problems and data-mining of semi-structured data.

A Strong Complete Schmema for Inductive Functional Logic Programming

A New IFLP schema is presented as a general framework for the induction of functional logic programs (FLP). Since narrowing (which is the most usual operational semantics of FLP) performs a unification (mgu) followed by a replacement, we introduce two main operators in our IFLP schema: a generalisation and an inverse replacement or intra-replacement, which results in a generic inversion of the transitive property of equality. We prove that this schema is strong complete in the way that, given some evidence, it is possible to induce any program which could have generated that evidence, We outline some possible restrictions in order to improve the tractability of the schema. We also show that inverse narrowing is just a special case of our IFLP schema. Finally, a straightforward extension of the IFLP schema to function invention is illustrated.

SMILES: A Multi-purpose Learning System

A machine learning system is useful for extracting models from data that can be used for many applications such as data analysis, decision support or data mining. SMILES is a machine learning system that integrates many different features from other machine learning techniques and paradigms, and more importantly, it presents several innovations in almost all of these features, such as ensemble methods, cost-sensitive learning, and the generation of a comprehensible model from an ensemble. This paper contains a short description of the main features of the system as well as some experimental results.

From Ensemble Methods to Comprehensible Models

Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. However, there are two important shortcomings associated with ensemble methods. Huge amounts of memory are required to store a set of multiple hypotheses and, more importantly, comprehensibility of a single hypothesis is lost. In this work, we devise a new method to extract one single solution from a hypothesis ensemble without using extra data, based on two main ideas: the selected solution must be similar, semantically, to the combined solution, and this similarity is evaluated through the use of a random dataset. We have implemented the method using shared ensembles, because it allows for an exponential number of potential base hypotheses. We include several experiments showing that the new method selects a single hypothesis with an accuracy which is reasonably close to the combined hypothesis.

Induction of Decision Multi-trees Using Levin Search

In this paper, we present a method for generating very expressiv e decision trees over a functional logic language. The generation of the tree follo ws a short-to-long search which is guided by the MDL principle. Once a solution is found, the construction of the tree goes on in order to obtain more solutions ordered as well by description length. The result is a multi-tree which is populated taking into consideration computational resources according to a Levin search. Some experiments show that the method pays off in practice.

Predictive Software

We examine the adaptation of classical machine learning selection criteria to ensure or improve the predictiveness of specifications. Moreover, inspired in incremental learning, software construction is also seen as an incremental process which must generate and revise the specification with the main goal of being predictive to requirements evolution. The new goal is not necessarily to achieve the highest accuracy at the end of a first prototype or version, but to maximise the cumulative benefits obtained throughout the entire software life-cycle. This suggests a new software life-cycle, whose main characteristic is to move modifications earlier, by using more eager inductive techniques, and reducing overall modification probability. This new predictive software life-cycle is particularised for the case of (functional) logic programming, placing the deductive/inductive techniques necessary for each stage of the life-cycle. The maturity of each stage and the practical possibilities for a (semi-)automation of the cycle based on declarative techniques are also discussed.

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the

Pentaho + R: An Integral View for Multidimensional Prediction Models

UCE