With hundreds of thousands of users, RapidMiner is the most frequently used visual workflow platform for machine learning. It covers the full spectrum of analytics from data preparation to machine learning and model validation. In this presentation, I will take you on a tour of machine learning which spans the last 15 years of research and industry applications and share key insights with you about how data scientists perform their daily analysis tasks. These patterns are extracted from mining millions of analytical workflows that have been created with RapidMiner over the past years. This talk will address important questions around the data mining process such as: What are the most frequently used solutions for typical data quality problems? How often are analysts using decision trees or neural networks? And does this behavior change over time or depend on the users experience level?

Web applications pose new security-related challenges since attacks on web applications strongly differ from those on client-server applications. Traditional network-based firewall systems offer no protection against this kind of at-tacks since they occur on the application-level. The current solution is the manual definition of large sets of filtering rules which should prevent malicious attempts from being successful. We propose a new framework which should avoid this tedious work. The basic idea is the definition of a description language for positive security models taking the particularities of web applications into account. We then present adaptive techniques which employ this description language in order to describe the valid communication to a given web application. The simplicity of the description language allows the easy identification of unintentionally incorporated vulnerabilities. Experiments for several real-world web applications demonstrate the usefulness of the proposed approach.

Recently, evolutionary computation has been successfully integrated into statistical learning methods. A Support Vector Machine (SVM) using evolution strategies for its optimization problem frequently deliver better results with respect to the optimization criterion and the prediction accuracy. Moreover, evolutionary computation allows for the efficient large margin optimization of a huge family of new kernel functions, namely non-positive semi definite kernels as the Epanechnikov kernel. For these kernel functions, evolutionary SVM even outperform other learning methods like the Relevance Vector Machine. In this paper, we will discuss another major advantage of evolutionary SVM compared to traditional SVM solutions: we can explicitly optimize the inherent trade-off between training error and model complexity by embedding multi-objective optimization into the evolutionary SVM. This leads to three advantages: first, it is no longer necessary to tune the SVM parameter C which weighs both conflicting criteria. This is a very time-consuming task for traditional SVM. Second, the shape and size of the Pareto front give interesting insights about the complexity of the learning task at hand. Finally, the user can actually see the point where overfitting occurs and can easily select a solution from the Pareto front best suiting his or her needs.

In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems. We then embed evolutionary computation into the most prominent representative of this class of learning methods, namely into Support Vector Machines (SVM). In contrast to former applications of evolutionary algorithms to SVMs we do not only optimize the method or kernel parameters. We rather use both evolution strategies and particle swarm optimization in order to directly solve the posed constrained optimization problem. Transforming the problem into the Wolfe dual reduces the total runtime and allows the usage of kernel functions. Exploiting the knowledge about this optimization problem leads to a hybrid mutation which further decreases convergence time while classification accuracy is preserved. We will show that evolutionary SVMs are at least as accurate as their quadratic programming counterparts on six real-world benchmark data sets. The evolutionary SVM variants frequently outperform their quadratic programming competitors. Additionally, the proposed algorithm is more generic than existing traditional solutions since it will also work for non-positive semidefinite kernel functions and for several, possibly competing, performance criteria.

In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multi-objective and behaves differently from supervised feature selection in that the number of features must be maximized instead of being minimized. Although this might sound surprising from a supervised learning point of view, we exemplify this relationship on the problem of data clustering and show that existing approaches do not pose the optimization problem in an appropriate way. Another important consequence of this paradigm change is a method which segments the Pareto sets produced by our approach. Inspecting only prototypical points from these segments drastically reduces the amount of work for selecting a final solution. We compare our methods against existing approaches on eight data sets.

KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring the development of new methods or processing schemes. Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and testing. Rapid prototyping is an approach which allows crucial design decisions as early as possible. A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new ones.This paper describes Yale, a free open-source environment forKDD and machine learning. Yale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary. Additionally, Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool. Following the paradigm of visual programming eases the design of processing schemes. While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phase.After a discussion of the key concepts of Yale, we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization. These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining. This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months.

Data mining in large collections of polyphonic music has recently received increasing interest by companies along with the advent of commercial online distribution of music. Important applications include the categorization of songs into genres and the recommendation of songs according to musical similarity and the customer's musical preferences. Modeling genre or timbre of polyphonic music is at the core of these tasks and has been recognized as a difficult problem. Many audio features have been proposed, but they do not provide easily understandable descriptions of music. They do not explain why a genre was chosen or in which way one song is similar to another. We present an approach that combines large scale feature generation with meta learning techniques to obtain meaningful features for musical similarity. We perform exhaustive feature generation based on temporal statistics and train regression models to summarize a subset of these features into a single descriptor of a particular notion of music. Using several such models we produce a concise semantic description of each song. Genre classification models based on these semantic features are shown to be better understandable and almost as accurate as traditional methods.

Designing technical plants is a complex and demanding process. It has been shown that the optimization of the simple facility placement problem is already NP-hard. Optimization of plant designs must obey a number of criteria derived from several fields of process engineering. We discuss an expansion of the simple facility placement problem with non-regular floor spaces and more than one layer. Additionally, we allow forbidden zones and predefined ways. In contrast to other approaches our system can cope with competitive criteria. These can be defined by a plant designer in an intuitive way according to concepts from fuzzy logic. This leads to the multiobjective optimization of costs and fulfillment of weighted design rules. We describe an evolutionary algorithm to construct Pareto-optimal blueprints of chemical plants. The smart indexing of rules and assignment of conclusions to components allows an efficient calculation of the rule fulfillment as part of the fitness function. Optimized blueprints for a real existing chemical plant dominate the original design.