Katharina Morik

Automatic Feature Extraction for Classifying Audio Data

Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew.This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences.

A Polynomial Approach to the Constructive Induction of Structural Knowledge

The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation, term subsumption formalisms have been developed which are efficient and expressive. In this article, a learning algorithm, KLUSTER, is described that represents concept definitions in this formalism. KLUSTER enhances the representation language if this is necessary for the discrimination of concepts. Hence, KLUSTER is a constructive induction program. KLUSTER builds the most specific generalization and a most general discrimination in polynomial time. It embeds these concept learning problems into the overall task of learning a hierarchy of concepts.

The MiningMart Approach to Knowledge Discovery in Databases

Although preprocessing is one of the key issues in data analysis, it is still common practice to address this task by manually entering SQL statements and using a variety of stand-alone tools. The results are not properly documented and hardly re-usable. The MiningMart system presented in this chapter focuses on setting up and re-using best practice cases of preprocessing data stored in very large databases. A metadata model named M4 is used to declaratively define and document both, all steps of such a preprocessing chain and all the data involved. For data and applied operators there is an abstract level, understandable by human users, and an executable level, used by the metadata compiler to run cases for given data sets. An integrated environment allows for rapid development of preprocessing chains. Adaptation to different environments is supported simply by specifying all involved database entities in the target DBMS. This allows reuse of best practice cases published on the Internet.

Accurate prediction of neuroblastoma outcome based on miRNA expression profiles.

For neuroblastoma, the most common extracranial tumour of childhood, identification of new biomarkers and potential therapeutic targets is mandatory to improve risk stratification and survival rates. MicroRNAs are deregulated in most cancers, including neuroblastoma. In this study, we analysed 430 miRNAs in 69 neuroblastomas by stem‐loop RT‐qPCR. Prediction of event‐free survival (EFS) with support vector machines (SVM) and actual survival times with Cox regression‐based models (CASPAR) were highly accurate and were independently validated. SVM‐accuracy for prediction of EFS was 88.7% (95% CI: 88.5–88.8%). For CASPAR‐based predictions, 5y‐EFS probability was 0.19% (95% CI: 0–38%) in the CASPAR‐predicted short survival group compared with 0.78% (95%CI: 64–93%) in the CASPAR‐predicted long survival group. Both classifiers were validated on an independent test set yielding accuracies of 94.74% (SVM) and 5y‐EFS probabilities as 0.25 (95% CI: 0.0–0.55) for short versus 1 ± 0.0 for long survival (CASPAR), respectively. Amplification of the MYCN oncogene was highly correlated with deregulation of miRNA expression. In addition, 37 miRNAs correlated with TrkA expression, a marker of excellent outcome, and 6 miRNAs further analysed in vitro were regulated upon TrkA transfection, suggesting a functional relationship. Expression of the most significant TrkA‐correlated miRNA, miR‐542‐5p, also discriminated between local and metastatic disease and was inversely correlated with MYCN amplification and event‐free survival. We conclude that neuroblastoma patient outcome prediction using miRNA expression is feasible and effective. Studies testing miRNA‐based predictors in comparison to and in combination with mRNA and aCGH information should be initiated. Specific miRNAs (e.g., miR‐542‐5p) might be important in neuroblastoma tumour biology, and qualify as potential therapeutic targets.

Knowledge Discovery and Knowledge Validation in Intensive Care

Operational protocols are a valuable means for quality control. However, developing operational protocols is a highly complex and costly task. We present an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that support the development of operational protocols. The aim is to ensure high quality standards for the protocol through empirical validation during the development, as well as lower development cost through the use of machine learning and statistical techniques. We demonstrate our approach of integrating expert knowledge with data driven techniques based on our effort to develop an operational protocol for the hemodynamic system.

Balanced Cooperative Modeling

Machine learning techniques are often used for supporting a knowledge engineer in constructing a model of part of the world. Different learning algorithms contribute to different tasks within the modeling process. Integrating several learning algorithms into one system allows it to support several modeling tasks within the same framework. In this article, we focus on the distribution of work between several learning algorithms on the one hand and the user on the other hand. The approach followed by the MOBAL system is that ofbalanced cooperation, i.e., each modeling task can be done by the user or by a learning tool of the system. The MOBAL system is described in detail. We discuss the principle of multi-functionality of one representation for the balanced use by learning algorithms and users.

Acquiring domain models

Abstract Whereas a Learning Apprentice System stresses the generation and refinement of shallow rules of a performance program, presupposing a domain theory, BLIP‡ is mainly concerned with the construction of a domain theory as the first phase of the knowledge-acquisition process. In this paper the BLIP approach to machine learning is described. The system design is presented and the already implemented knowledge sources are shown with their formalisms and functions for the learning process.

Learning concepts from sensor data of a mobile robot

Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robots high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars.

Knowledge representation and organization in machine learning

Explanation: A source of guidance for knowledge representation.- (Re)presentation issues in second generation expert systems.- Some aspects of learning and reorganization in an analogical representation.- A knowledge-intensive learning system for document retrieval.- Constructing expert systems as building mental models or toward a cognitive ontology for expert systems.- Sloppy modeling.- The central role of explanations in disciple.- An inference engine for representing multiple theories.- The acquisition of model-knowledge for a model-driven machine learning approach.- Using attribute dependencies for rule learning.- Learning disjunctive concepts.- The use of analogy in incremental SBL.- Knowledge base refinement using apprenticeship learning techniques.- Creating high level knowledge structures from simple elements.- Demand-driven concept formation.

Local Pattern Detection

Pushing Constraints to Detect Local Patterns.- From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms.- Pattern Discovery Tools for Detecting Cheating in Student Coursework.- Local Pattern Detection and Clustering.- Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery.- Visualizing Very Large Graphs Using Clustering Neighborhoods.- Features for Learning Local Patterns in Time-Stamped Data.- Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis.- Local Pattern Discovery in Array-CGH Data.- Learning with Local Models.- Knowledge-Based Sampling for Subgroup Discovery.- Temporal Evolution and Local Patterns.- Undirected Exception Rule Discovery as Local Pattern Detection.- From Local to Global Analysis of Music Time Series.