Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

Knowledge Management

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

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Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Photoinhibition of Photosystem II (PSII) is the light-induced loss of PSII electron-transfer activity. Although photoinhibition has been studied for a long time, there is no consensus about its mechanism. On one hand, production of singlet oxygen ((1)O(2)) by PSII has promoted models in which this reactive oxygen species (ROS) is considered to act as the agent of photoinhibitory damage. These chemistry-based models have often not taken into account the photophysical features of photoinhibition-like light response and action spectrum. On the other hand, models that reproduce these basic photophysical features of the reaction have not considered the importance of data about ROS. In this chapter, it is shown that the evidence behind the chemistry-based models and the photophysically oriented models can be brought together to build a mechanism that confirms with all types of experimental data. A working hypothesis is proposed, starting with inhibition of the manganese complex by light. Inability of the manganese complex to reduce the primary donor promotes recombination between the oxidized primary donor and Q(A), the first stable quinone acceptor of PSII. (1)O(2) production due to this recombination may inhibit protein synthesis or spread the photoinhibitory damage to another PSII center. The production of (1)O(2) is transient because loss of activity of the oxygen-evolving complex induces an increase in the redox potential of Q(A), which lowers (1)O(2) production.

Photoinhibition of Photosystem II

Semi-automatic data mining approaches often yield better results than plain automatic methods, due to the early integration of the user’s goals. For example in the medical domain, experts are likely to favor simpler models instead of more complex models. Then, the accuracy of discovered patterns is often not the only criterion to consider. Instead, the simplicity of the discovered knowledge is of prime importance, since this directly relates to the understandability and the interpretability of the learned knowledge.

In this paper, we present quality measures considering the understandability and the accuracy of (learned) rule bases. We describe a unifying quality measure, which can trade-off small losses concerning accuracy vs. an increased simplicity. Furthermore, we introduce a semi-automatic data mining method for learning understandable and accurate rule bases. The presented work is evaluated using cases from a real world application in the medical domain.

Quality measures and semi-automatic mining of diagnostic rule bases

Usually, domain knowledge is available at different levels of formality, for example such as documents, data bases, and (business) rules. We argue, that today’s systems limit the knowledge engineering process to a fixed level of formality and expressiveness, respectively, and that these limitations hinder effective knowledge acquisition and use. In consequence, we introduce the knowledge formalization continuum as a metaphor, that embraces the fact that knowledge is available in different formalities. We motivate that a semantic wiki is a suitable tool to work on the knowledge formalization continuum, and we introduce KnowWE as an example wiki implementation.

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Engineering on the Knowledge Formalization Continuum.

Das Thema ,,Semantic Wikis“ als Verknupfung von Wiki-Konzepten mit semantischen Technologien gewann in den vergangenen Jahren zunehmend an Bedeutung, weil es ,,soziale Intelligenz“ und ,,kunstliche Intelligenz“ miteinander verknupft. Ziel des Artikels ist es, einen Uberblick uber das Thema Semantic Wikis zu schaffen. Wir gehen dabei insbesondere auch auf Anwendungsfelder ein und geben einen kurzen Vergleich ausgewahlter Systeme.

Semantic Wiki

Agile Development of Rule Systems

With the success of numerous applications of the Web 2.0 the interest in a web–based support of scientific communities has also gained significant relevance. The concept of Wikis, one building block of the Web 2.0, has shown to be a reasonable infrastructure for sharing and refining any kind of knowledge. The most prominent example is the encyclopedia Wikipedia, but many smaller wiki applications have proved to be beneficial, e.g., the availability of experience management and documentation projects in open– source communities and large enterprises. In this paper, we introduce knowledge wikis that extend the features of a regular wiki by the representation and use of explicit problem–solving knowledge. We motivate the general ideas and benefits of knowledge wikis, and we describe a concrete knowledge wiki implementation. The actual use of a knowledge wiki is demonstrated by two case studies: the first case study reports on the share and reuse of ecological domain knowledge in the context of the BIOLOG project; the second case study describes a knowledge formalization pattern repository taken as a demonstrator from the knowledge engineering domain.

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Joachim Baumeister

Papers

Quality measures and semi-automatic mining of diagnostic rule bases

Engineering on the Knowledge Formalization Continuum.

Semantic Wiki

Agile Development of Rule Systems

Using Knowledge Wikis to Support Scientific Communities.