Sobre el sustantivo masculino con rasgo de sexo

En español, como en muchos otros lenguajes, en aquellos pares de nombres opuestos por género (masculino femenino) y cuyo significado puede ser de género masculino o femenino no funciona de la misma forma. En nombres femeninos, la característica “hembra” está siempre presente, mientras que en los masculinos, la característica “macho”, no. A menudo este rasgo de sexo es neutralizado. En este artículo, basando nuestro estudio en estos pares de nombres, analizamos las condiciones requeridas para la existencia o no del rasgo “macho” en nombres masculinos.In Spanish, as in many other languages, in those pairs of nouns opposed by gender (masculine/ feminine) and whose meaning can of masculine gender and that of feminine gender do not work in the same way. In feminine nouns, the feature «female» is always present, whereas in masculine nouns the feature «male» is not; very often this feature of sex is neutralized. In this paper, basing our study on these pairs of nouns, we analyse the conditions required for the existence or non existence of the feature «male» in masculine nouns

Análisis discursivo de las oraciones subordinadas sustantivas: información y argumentación

La estructuración de la oración subordinada sustantiva (OSS) en el seno de la oración tiene un alto grado de formalización, bien descrito en la gramática, también en la de la lengua española: la OSS es una unidad funcional de estructura interna oracional, que deja de ser oración en cuanto a sus relaciones externas, al ocupar una estructura nominal que funciona como papel temático en una oración o como complemento nominal en el seno de un SN Sin embargo, es preciso destacar que, en ciertas construcciones, la explicación de la presencia de una OSS en el seno de una oración, precisa trascender el marco oracional en el que se halla inserta. Se trata de algunas construcciones oracionales capacitadas para la inserción de OSS, cuyo contenido establece unas determinadas relaciones discursivas con alguna secuencia previa a la oración. Cuando el contenido de una cadena oracional contrae relaciones de enunciación con parte del discurso actualizado se dan funciones informativas y argumentativas específicas: el emisor valora, justifica, enjuicia contenidos previos o establece relaciones lógicas, contribuyendo a realzar la función argumentativa; o, en el nivel in-formativo, contribuye a destacar la función temática mediante la OSS, siempre en construcciones factivas

On the design of an ECOC-compliant genetic algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches

Differential Replication for Credit Scoring in Regulated Environments

Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solution

Towards Global Explanations for Credit Risk Scoring

In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the original classification accuracy. We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing

Environmental adaptation and differential replication in machine learning

When deployed in the wild, machine learning models are usually confronted withan environment that imposes severe constraints. As this environment evolves, so do these constraints.As a result, the feasible set of solutions for the considered need is prone to change in time. We referto this problem as that of environmental adaptation. In this paper, we formalize environmentaladaptation and discuss how it differs from other problems in the literature. We propose solutionsbased on differential replication, a technique where the knowledge acquired by the deployed modelsis reused in specific ways to train more suitable future generations. We discuss different mechanismsto implement differential replications in practice, depending on the considered level of knowledge.Finally, we present seven examples where the problem of environmental adaptation can be solvedthrough differential replication in real-life applications

Copying Machine Learning Classifiers

We study copying of machine learning classifiers, an agnostic technique to replicate the decision behavior of any classifier. We develop the theory behind the problem of copying, highlighting its properties, and propose a framework to copy the decision behavior of any classifier using no prior knowledge of its parameters or training data distribution. We validate this framework through extensive experiments using data from a series of well-known problems. To further validate this concept, we use three different use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics