Data Mining Practical Machine Learning Tools and Techniques

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Introduction to linear and nonlinear programming

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronou...

Automated Algorithm Selection: Survey and Perspectives

Exploratory Landscape Analysis subsumes a number of techniques employed to obtain knowledge about the properties of an unknown optimization problem, especially insofar as these properties are important for the performance of optimization algorithms. Where in a first attempt, one could rely on high-level features designed by experts, we approach the problem from a different angle here, namely by using relatively cheap low-level computer generated features. Interestingly, very few features are needed to separate the BBOB problem groups and also for relating a problem to high-level, expert designed features, paving the way for automatic algorithm selection.

Exploratory landscape analysis

In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible The R2 and the Hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist In this paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way The influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented

/pdf/on-the-properties-of-the-r2-indicator-hhcvp2l3m3.pdf

On the properties of the R2 indicator

The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB'09/10 workshop.

/pdf/algorithm-selection-based-on-exploratory-landscape-analysis-50td0yjbds.pdf

Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.

Heike Trautmann

Papers

Automated Algorithm Selection: Survey and Perspectives

Exploratory landscape analysis

On the properties of the R2 indicator

Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

Resampling methods for meta-model validation with recommendations for evolutionary computation