28 research outputs found
Intuition is programmable
Valedictory Address presented in abbreviated form at the public farewell to the office of Professor of Computer Science at the Tilburg center for Cognition and Communication (TiCC) of the Faculty of Humanities of Tilburg University on Friday, January 29th, 2016.FdR – Publicaties zonder aanstelling Universiteit Leide
Computing behind the iron curtain and beyond hungarian national perspective
At the beginning of the computing era it was a great challenge to write a running program in order to solve different mathematical, statistical tasks, and to get the appropriate results by using a computer. At the university where I had began my studies we started to get acquainted with computers using a Russian made machine, named URAL 2. Looking back to the roots and thinking of the Hungarian born pioneer of computer science: the polymath John von Neumann, I had intended to deal with the history of computing at the very beginning of the eighties. Although there is a wide range of computing history literature worldwide (most of them in English), there are not any publications dealing with the Hungarian results. As I was one of the first graduates on computing taught by the Pioneers, I am forced to perform research on all materials dealing with the Hungarian achievements in computing, to protect the values of the past as long as it is not too late and also to make the results known!2nd IFIP Conference on the History of Computing and EducationRed de Universidades con Carreras en Informática (RedUNCI
A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data
The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results
A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications with Imbalanced Data
The current financial crisis has
stressed the need of obtaining more accurate
prediction models in order to decrease the risk when
investing money on economic opportunities. In
addition, the transparency of the process followed to
make the decisions in financial applications is
becoming an important issue. Furthermore, there is a
need to handle the real-world imbalanced financial
data sets without using sampling techniques which
might introduce noise in the used data. In this paper,
we present a compact evolutionary interval-valued
fuzzy rule-based classification system, which is
based on IVTURSFARC-HD (Interval-Valued fuzzy rulebased classification system with TUning and Rule
Selection) [22]), for the modeling and prediction of
real-world financial applications. This proposed
system allows obtaining good predictions accuracies
using a small set of short fuzzy rules implying a high
degree of interpretability of the generated linguistic
model. Furthermore, the proposed system deals with
the financial imbalanced datasets with no need for
any preprocessing or sampling method and thus
avoiding the accidental introduction of noise in the
data used in the learning process. The system is also
provided with a mechanism to handle examples that
are not covered by any fuzzy rule in the generated
rule base. To test the quality of our proposal, we will
present an experimental study including eleven realworld financial datasets. We will show that the
proposed system outperforms the original C4.5
decision tree, type-1 and interval-valued fuzzy
counterparts which use the SMOTE sampling
technique to preprocess data and the original FURIA,
which is a fuzzy approximative classifier.
Furthermore, the proposed method enhances the
results achieved by the cost sensitive C4.5 and it
gives competitive results when compared with
FURIA using SMOTE, while our proposal avoids
pre-processing techniques and it provides
interpretable models that allow obtaining more
accurate results.Spanish Government
TIN2011-28488
TIN2013-40765-