A Bayesian Approach to Learning Scoring Systems

We present a Bayesian method for building scoring systems, which are linear models with coefficients that have very few significant digits. Usually the construction of scoring systems involve manual efforthumans invent the full scoring system without using data, or they choose how logistic regression coefficients should be scaled and rounded to produce a scoring system. These kinds of heuristics lead to suboptimal solutions. Our approach is different in that humans need only specify the prior over what the coefficients should look like, and the scoring system is learned from data. For this approach, we provide a Metropolis-Hastings sampler that tends to pull the coefficient values toward their natural scale. Empirically, the proposed method achieves a high degree of interpretability of the models while maintaining competitive generalization performances.


Improved state estimation for jump Markov linear systems
Orguner, Umut; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2005)
This thesis presents a comprehensive example framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as: -Design of multiple model state estimation algorithms using new criteria. -Improvements obtained using existing multiple model state estimation algorithms. In the first category, risk-sensitive estimation is proposed for jump Markov linear systems. Two types of cost functions namely, the instantaneous an...
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03)
In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
A new contribution to nonlinear robust regression and classification with mars and its applications to data mining for quality control in manufacturing
Yerlikaya, Fatma; Weber, Gerhard Wilhelm; Department of Scientific Computing (2008)
Multivariate adaptive regression spline (MARS) denotes a modern methodology from statistical learning which is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. MARS is very useful for high dimensional problems and shows a great promise for fitting nonlinear multivariate functions. MARS technique does not impose any particular class of relationship between the predictor variables and outcome variable of interest....
Applications of hybrid discrete Fourier transform-moment method to the fast analysis of large rectangular dipole arrays printed on a thin grounded dielectric substrate
Chou, HT; Ko, HK; Aydın Çivi, Hatice Özlem; ERTÜRK, VAKUR BEHÇET (2002-08-05)
Recently a discrete Fourier transform-method of moments (DFT-MoM) scheme was developed for fast analysis of electrically large rectangular planar dipole arrays, which has been shown to be very efficient in terms of number reduction of unknown variables and computational complexity. The applications of this DFT-MoM to treat dipole arrays printed on a grounded dielectric substrate are examined in this Letter. Numerical results are presented to validate its efficiency and accuracy. (C) 2002 Wiley Periodicals, ...
Parallel-MLFMA Solutions of Large-Scale Problems Involving Composite Objects
Ergül, Özgür Salih (2012-07-14)
We present a parallel implementation of the multilevel fast multipole algorithm (MLFMA) for fast and accurate solutions of large-scale electromagnetics problems involving composite objects with dielectric and metallic parts. Problems are formulated with the electric and magnetic current combined-field integral equation (JMCFIE) and solved iteratively with MLFMA on distributed-memory architectures. Numerical examples involving canonical and complicated objects, such as optical metamaterials, are presented to...
Citation Formats
Ş. Ertekin Bolelli, “A Bayesian Approach to Learning Scoring Systems,” BIG DATA, pp. 267–276, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42890.