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

In the last years, ontologies have played a major role for building large, distributed, and heterogeneous intelligent systems. E.g., ontologies are one key technique of the semantic web layer. The development process of an ontology heavily depends on its evaluation. In this paper, we introduce several measures for the evaluation of ontological knowledge. Besides standard methods like taxonomic errors we also present novel metrics focusing on design anomalies. For the implementation of these measures we propose a declarative approach using the logic-based language FNQuery.

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Smelly Owls - Design Anomalies in Ontologies

When knowledge systems are deployed into a real-world application, then the maintenance of the knowledge is a crucial success factor. In the past, some approaches for the automatic refinement of knowledge bases have been proposed. Many only provide limited control during the modification and refinement process, and often assumptions about the correctness of the knowledge base and case base are made. However, such assumptions do not necessarily hold for real-world applications.

In this paper, we present a novel interactive approach for the user-guided refinement of knowledge bases. Subgroup mining methods are used to discover local patterns that describe factors potentially causing incorrect behavior of the knowledge system. We provide a case study of the presented approach with a fielded system in the medical domain.

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Subgroup mining for interactive knowledge refinement

Decision support systems rely on collaborative and episodic participation.Large decision knowledge bases can include varying knowledge representations.We introduce an approach to implement such systems in a coherent manner.The approach is based on semantic technologies and web standards.A case study describes experiences with a decision support system in industrial use for years. The paper describes a new approach of developing and maintaining state-of-the-art decision support systems. Such systems are able to capture the collaborative work on decision problems over time. Due to the complexity of large problem spaces a multi-modal knowledge representation is proposed. For the realization of a multi-modal knowledge base we integrate semantic technologies as a fundamental layer by combining the W3C ontologies PROV-O and SKOS. The approach is demonstrated by an implementation report of an industrially deployed decision support system.

Knowledge-driven systems for episodic decision support

Currently, the introduction of an appropriate rule representation layer for the semantic web stack is discussed. However, with the inclusion of rule-based knowledge new verification issues for rule-augmented ontologies arise.

In this paper we investigate the detection of anomalies as an important subtask of verification. We extend and revise existing approaches for the syntactic verification of ontologies with respect to the existence of rules, and we introduce new anomalies considering the understandability and maintainability of such ontologies.

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Joachim Baumeister

Papers

Smelly Owls - Design Anomalies in Ontologies

Subgroup mining for interactive knowledge refinement

Knowledge-driven systems for episodic decision support

Verification and refactoring of ontologies with rules

An expert system to estimate the pesticide contamination of small streams using benthic macroinvertebrates as bioindicators