Theory and Applications of Robust Optimization

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.Comment: 50 page

A generic optimising feature extraction method using multiobjective genetic programming

In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved

Dynamic Linear Discriminant Analysis in High Dimensional Space

High-dimensional data that evolve dynamically feature predominantly in the modern data era. As a partial response to this, recent years have seen increasing emphasis to address the dimensionality challenge. However, the non-static nature of these datasets is largely ignored. This paper addresses both challenges by proposing a novel yet simple dynamic linear programming discriminant (DLPD) rule for binary classification. Different from the usual static linear discriminant analysis, the new method is able to capture the changing distributions of the underlying populations by modeling their means and covariances as smooth functions of covariates of interest. Under an approximate sparse condition, we show that the conditional misclassification rate of the DLPD rule converges to the Bayes risk in probability uniformly over the range of the variables used for modeling the dynamics, when the dimensionality is allowed to grow exponentially with the sample size. The minimax lower bound of the estimation of the Bayes risk is also established, implying that the misclassification rate of our proposed rule is minimax-rate optimal. The promising performance of the DLPD rule is illustrated via extensive simulation studies and the analysis of a breast cancer dataset.Comment: 34 pages; 3 figure

Effectiveness of R&D project selection in uncertain environment: An empirical study in the German automotive supplier industry

This paper presents results of an empirical large-scale study on uncertainty reduction of R&D projects and R&D project selection. The empirical field is the German automotive supplier industry. We explore R&D project selection practices in this specific industry and briefly contrast our findings with the academic research and management literature in this field. We concentrate on answering three research questions (with focus on questions no. 1 and 2): I. Which information and related uncertainties are crucial for the product selection decision to the R&D decision makers? II. How do R&D decision makers today cope with typical challenges related to reducing uncertainty? Where do they face major problems and how effective are they? III. What are major implications for managing the Fuzzy Front End (FFE) of innovation process in industry practice and respectively for further academic research in this field? Key findings are that on the one hand certainty about fields of product applications, target markets and production feasibility are most important criteria for initial product selection decisions. On the other hand market and cost related uncertainties (e.g. sales volume, product price, cost per unit) cannot be satisfyingly reduced in practice before project approval for development or definite termination of projects. Although different uncertainty profiles exist within the process of project evaluation, most companies do not systematically choose available product selection methods and tools according to specific uncertainty situations. Intuition still plays a major role in R&D product selection. Some first conclusion drawn from this research are: A sufficient level of resources (including financial and methodological know-how), a systematic use of suitable project selection instruments, and a fit with the company specific as well as the OEMs' product/brand strategies can be potential levers for more effective uncertainty reduction before product decision. --

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ