A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set

For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate classification is beneficial to lenders in terms of increased financial profits or reduced losses and to loan applicants who can avoid overcommitment. This paper examines a historical data set from consumer loans issued by a German bank to individuals whom the bank considered to be qualified customers. The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper examines and compares the classification accuracy rates of three decision tree techniques as well as analyzes their ability to generate easy to understand rules

Maximum likelihood estimation by monte carlo simulation:Toward data-driven stochastic modeling

We propose a gradient-based simulated maximum likelihood estimation to estimate unknown parameters in a stochastic model without assuming that the likelihood function of the observations is available in closed form. A key element is to develop Monte Carlo-based estimators for the density and its derivatives for the output process, using only knowledge about the dynamics of the model. We present the theory of these estimators and demonstrate how our approach can handle various types of model structures. We also support our findings and illustrate the merits of our approach with numerical results

Neural processes of proactive and reactive controls modulated by motor-skill experiences

This study investigated the experience of open and closed motor skills on modulating proactive and reactive control processes in task switching. Fifty-four participants who were open-skilled

Squeezing and entanglement delay using slow light

We examine the interaction of a weak probe with $N$ atoms in a lambda-level configuration under the conditions of electromagnetically induced transparency (EIT). In contrast to previous works on EIT, we calculate the output state of the resultant slowly propagating light field while taking into account the effects of ground state dephasing and atomic noise for a more realistic model. In particular, we propose two experiments using slow light with a nonclassical probe field and show that two properties of the probe, entanglement and squeezing, characterizing the quantum state of the probe field, can be well-preserved throughout the passage.Comment: 2 figures; v2: fixed some minor typographical errors in a couple of equations and corrected author spelling in one reference. v3: Added three authors; changed the entaglement definition to conform to a more accepted standard (Duan's entanglement measure); altered the abstract slightly. v4: fixed formatting of figure

Can optical squeezing be generated via polarization self-rotation in a thermal vapour cell?

The traversal of an elliptically polarized optical field through a thermal vapour cell can give rise to a rotation of its polarization axis. This process, known as polarization self-rotation (PSR), has been suggested as a mechanism for producing squeezed light at atomic transition wavelengths. In this paper, we show results of the characterization of PSR in isotopically enhanced Rubidium-87 cells, performed in two independent laboratories. We observed that, contrary to earlier work, the presence of atomic noise in the thermal vapour overwhelms the observation of squeezing. We present a theory that contains atomic noise terms and show that a null result in squeezing is consistent with this theory.Comment: 10 pages, 11 figures, submitted to PRA. Please email author for a PDF file if the article does not appear properl

Vortex lines in the three-dimensional XY model with random phase shifts

The stability of the ordered phase of the three-dimensional XY-model with random phase shifts is studied by considering the roughening of a single stretched vortex line due to the disorder. It is shown that the vortex line may be described by a directed polymer Hamiltonian with an effective random potential that is long range correlated. A Flory argument estimates the roughness exponent to $\zeta=3/4$ and the energy fluctuation exponent to $\omega=1/2$, thus fulfilling the scaling relation $\omega=2\zeta-1$. The Schwartz-Edwards method as well as a numerical integration of the corresponding Burger's equation confirm this result. Since $\zeta<1$ the ordered phase of the original XY-model is stable.Comment: 8 pages RevTeX, 3 eps-figures include

Exploring the Partonic Structure of Hadrons through the Drell-Yan Process

The Drell-Yan process is a standard tool for probing the partonic structure of hadrons. Since the process proceeds through a quark-antiquark annihilation, Drell-Yan scattering possesses a unique ability to selectively probe sea distributions. This review examines the application of Drell-Yan scattering to elucidating the flavor asymmetry of the nucleon's sea and nuclear modifications to the sea quark distributions in unpolarized scattering. Polarized beams and targets add an exciting new dimension to Drell-Yan scattering. In particular, the two initial-state hadrons give Drell-Yan sensitivity to chirally-odd transversity distributions.Comment: 23 pages, 9 figures, to appear in J. Phys. G, resubmission corrects typographical error