Using rule extraction to improve the comprehensibility of predictive models.

Whereas newer machine learning techniques, like artifficial neural net-works and support vector machines, have shown superior performance in various benchmarking studies, the application of these techniques remains largely restricted to research environments. A more widespread adoption of these techniques is foiled by their lack of explanation capability which is required in some application areas, like medical diagnosis or credit scoring. To overcome this restriction, various algorithms have been proposed to extract a meaningful description of the underlying `blackbox' models. These algorithms' dual goal is to mimic the behavior of the black box as closely as possible while at the same time they have to ensure that the extracted description is maximally comprehensible. In this research report, we first develop a formal definition of`rule extraction and comment on the inherent trade-off between accuracy and comprehensibility. Afterwards, we develop a taxonomy by which rule extraction algorithms can be classiffied and discuss some criteria by which these algorithms can be evaluated. Finally, an in-depth review of the most important algorithms is given.This report is concluded by pointing out some general shortcomings of existing techniques and opportunities for future research.Models; Model; Algorithms; Criteria; Opportunities; Research; Learning; Neural networks; Networks; Performance; Benchmarking; Studies; Area; Credit; Credit scoring; Behavior; Time;

ITER: An algorithm for predictive regression rule extraction. Data warehousing and knowledge discovery. Proceedings.

Various benchmarking studies have shown that artificial neural networks and support vector machines have a superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the motivation behind these models' decisions. Various rule extraction techniques have been proposed to overcome this opacity restriction. However, most of these extraction techniques are devised for classification and only few algorithms can deal with regression problems.

New trends in data mining.

Trends; Data; Data mining;

A comprehensible SOM-based scoring system.

The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in 'good' and 'bad' risk categories. Traditionally, (logistic) regression used to be one of the most popular methods for this task, but recently some newer techniques like neural networks and support vector machines have shown excellent classification performance. Self-organizing maps (SOMs) have existed for decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. In this paper, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Classification accuracy of the models is benchmarked with results reported previously.Decision; Knowledge; Knowledge discovery; Systems; Growth; Credit; Methods; Risk; Regression; Neural networks; Networks; Classification; Performance; Area; Research; Credit scoring; Models; Model;

Country corruption analysis with self organizing maps and support vector machines.

During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle this research field from a data mining point of view. In this paper, we apply data mining techniques onto a cross-country database linking macro-economical variables to perceived levels of corruption. In the first part, self organizing maps are applied to study the interconnections between these variables. Afterwards, support vector machines are trained on part of the data and used to forecast corruption for other countries. Large deviations for specific countries between these models' predictions and the actual values can prove useful for further research. Finally, projection of the forecasts onto a self organizing map allows a detailed comparison between the different models' behavior.cross-country;

The healing pattern of osteoid osteomas on computed tomography and magnetic resonance imaging after thermocoagulation

Objective To compare the healing pattern of osteoid osteomas on computed tomography (CT) and magnetic resonance imaging (MRI) after successful and unsuccessful thermocoagulation. Materials and methods Eighty-six patients were examined by CT and 18 patients by dynamic gadolinium-enhanced MRI before and after thermocoagulation for osteoid osteoma. Thermocoagulation was successful in 73% (63/86) and unsuccessful in 27% (23/86) of patients followed by CT. Thermocoagulation was successful in 72% (13/18) of patients followed by MRI. After treatment, the healing of the nidus on CT was evaluated using different healing patterns (complete ossification, minimal nidus rest, decreased size, unchanged size or thermonecrosis). On MRI the presence of reactive changes (joint effusion, "oedema-like" changes of bone marrow and soft tissue oedema) and the delay time (between arterial and nidus enhancement) were assessed and compared before and after thermocoagulation. Results Complete ossification or a minimal nidus rest was observed on CT in 58% (16/28) of treatment successes (with > 12 months follow-up), but not in treatment failures. "Oedema-like" changes of bone marrow and/or soft tissue oedema were seen on MR in all patients before thermocoagulation and in all treatment failures. However, residual "oedema-like" changes of bone marrow were also found in 69% (9/13) of treatment successes. An increased delay time was observed in 62% (8/13) of treatment successes and in 1/5 of treatment failures. Conclusion Complete, or almost complete, ossification of the treated nidus on CT correlated with successful treatment. Absence of this ossification pattern, however, did not correlate with treatment failure. CT could not be used to identify the activity of the nidus following treatment. The value of MR parameters to assess residual activity of the nidus was limited in this study

No Terroir in the Cold? : A Note on the Geography of Geographical Indications

First published:04 April 2019Geographical Indications (GIs) are increasingly important instruments of agricultural and food regulations and are growing as contentious issues in trade negotiations and disputes. GIs can improve welfare but they can also be a protectionist instrument. The EU has the most GIs in the world, but they are concentrated in the south of the EU. Even excluding wine, there are seven times more food GIs per capita in the southern EU Member States than in other EU Member States. This note discusses several factors which may explain the geographic concentration of GIs in the south of the EU.KU Leuven (Methusalem Program)Excellence of Science (EOS) Research project of FWOEUEuropean Union (EU

No Terroir in the Cold? A Note on the Geography of Geographical Indications

© 2019 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. Geographical Indications (GIs) are increasingly important instruments of agricultural and food regulations and are growing as contentious issues in trade negotiations and disputes. GIs can improve welfare but they can also be a protectionist instrument. The EU has the most GIs in the world, but they are concentrated in the south of the EU. Even excluding wine, there are seven times more food GIs per capita in the southern EU Member States than in other EU Member States. This note discusses several factors which may explain the geographic concentration of GIs in the south of the EU.status: publishe

Sharing Visual Information in Virtual Environments using Projective Texture Mapping ABSTRACT

We present a method for sharing visual information in 3D virtual environments, using a projective texture mapping based method. Avatars can share information with other avatars by projecting relevant information into the environment. In addition to standard projective texture mapping, an important depth cue is added: projected light is attenuated in function of the light-travel distance. This is efficiently accomplished on a per vertex basis by adaptively super-sampling under-sampled polygons. This way, the projection quality is maximized while keeping a fixed frame rate. Our technique is implemented into the Quake III engine, extending its shading language with GLSL fragment and vertex shaders. Categories and Subject Descriptors: H.5.1 [INFORMATION INTERFACES AND PRESENTA