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.

Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision. There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

An Introduction to MultiAgent Systems.

Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS-SVM for Unsupervised Learning LS-SVM for Recurrent Networks and Control.

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Least Squares Support Vector Machines

Recently, declarative process languages are gaining ground as the procedural way of capturing activities in a fixed work-flow is deemed to be inflexible. Declare, one of the prime languages of the declarative process modeling paradigm, is composed of logic-based activity constraints which are event-driven. In this paper, a template-based conversion is proposed of every Declare constraint into a single Petri net fragment with weighted, reset and inhibitor arcs, i.e. a weighted R/I-net. As such, a formalization of the execution semantics of Declare is obtained, similar to linear temporal logic or regular expressions, but now expressed in the form of Petri nets. Equivalence of Declare constraints and the respective Petri net templates are analyzed at the theoretical level and by means of a simulation experiment.

A full R/I-net construct lexicon for declare constraints

Business Environments, such as shops, offices, automation plants, etc., are usually handled by the modeling and execution of business processes using languages such as Colored Petri Nets (CPN). CPN are a mathematical and graphical modeling language with powerful analysis techniques. However, current Petri nets formalisms are not aware of their execution context (i.e., Users and equipment location, temporal and environmental properties, etc.) and therefore it is not possible to manage business processes whose execution adapts to its context. To support this adaptation and make business processes smarter, this paper extends CPN with ontologies. Ontologies are powerful and very expressive techniques for modeling context and storing its data at execution time. To be aware of the current context, a context manager is used to sense the execution context using ubicomp technologies and represent it in the ontologies. This solution not only allows the modeling and execution of context-adaptive Petri nets, but also improves system design, reuse, and maintenance by dealing with context and business behavior as separate concerns.

Making Business Environments Smarter: A Context-Adaptive Petri Net Approach

Decisions are often not adequately modeled. They are hardcoded in process models or other representations, but not modeled in a systematic way. Because of this hardcoding or inclusion in other models, organizations often lack the necessary flexibility, maintainability and traceability in their business operations. We propose to first model the structure of the decision. Next, starting from this declarative model, a set of processes are built or derived, which are ultimately evaluated against a set of business criteria. This approach aims to develop a roadmap for the modeling of business processes based on decisions structures, and examines the challenges that arise when such decision structures are eventually transformed into more optimal execution-system geared business processes.

Modeling business decisions and processes – which comes first?

As of this writing, there exists a large variety of recently developed pattern classification methods coming from the domain of machine learning and artificial intelligence. In this paper, we study the performance of a recently developed and improved classifier that integrates fuzzy set theory in a neural network (NEFCLASS). The performance of NEFCLASS is compared to a well-known classification technique from machine learning (C4.5). Both C4.5 and NEFCLASS will be evaluated on a collection of benchmarking data sets. Further, to boost performance of NEFCLASS, we investigate the advantage of preprocessing the algorithm by means of an exploratory factor analysis. We compare the algorithms before and after applying an exploratory factor analysis on leading performance indicators, as there are the accuracy of the created classifier and the magnitude of the associated rule base.

Improving a neuro-fuzzy classifier using exploratory factor analysis

Digital transformation is the rapidly expanding research field dealing with the increased impact of digital technologies on both business and society. Due to the large number of papers and the semantic ambiguity surrounding the terminology, covering such a broad topic is difficult. To help researchers gain a better understanding of the knowledge structure of the research field, we conduct a scoping review using scientometrics. We searched for publications dealing with digital transformation on both Scopus and Web of Science. We downloaded their bibliometric data and thoroughly merged and cleaned it using lemmatization and stemmatization. This dataset was analyzed using VOSviewer to create co-author networks and co-word occurrence graphs of the titles, abstracts, and keywords. We also visualized the growth of the research field and retrieved the top conferences and journals based on the number of papers and the number of citations. K-means clustering was performed on the abstracts and keywords to find similar research focuses. These findings highlight the broad scope of the research field, the ambiguity of the terminology, the lack of collaboration, and the absence of research into the impact of digital transformation on society. Moving forward, more research needs to be done to establish the boundaries of digital transformation and to investigate the importance of society in this phenomenon.

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Jan Vanthienen

Papers

A full R/I-net construct lexicon for declare constraints

Making Business Environments Smarter: A Context-Adaptive Petri Net Approach

Modeling business decisions and processes – which comes first?

Improving a neuro-fuzzy classifier using exploratory factor analysis

A Scoping Review of the Digital Transformation Literature Using Scientometric Analysis.