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

The processes of logistics service providers are considered as highly human-centric, flexible and complex. Deviations from the standard operating procedures as described in the designed process models, are not uncommon and may result in significant uncertainties. Acquiring insight in the dynamics of the actual logistics processes can effectively assist in mitigating the uncovered risks and creating strategic advantages, which are the result of uncertainties with respectively a negative and a positive impact on the organizational objectives. In this paper a comprehensive methodology for applying process mining in logistics is presented, covering the event log extraction and preprocessing as well as the execution of exploratory, performance and conformance analyses. The applicability of the presented methodology and roadmap is demonstrated with a case study at an important Chinese port that specializes in bulk cargo.

Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port

Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e.g., solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercer's theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands.

Bankruptcy prediction with least squares support vector machine classifiers

In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier.

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Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring

Declarative process models have become a mature alternative to procedural ones. Instead of focusing on what has to happen, they rather follow an outside-in approach based on a rule base containing different types of constraints. The models are well-capable of representing flexible behavior, as everything that is not forbidden by the constraints in the model is possible during execution. These models, however, are more difficult to comprehend and require a higher mental effort of both the modeler and the reader. Since constraints can be added freely to the model, it is often overseen what impact the combination of them has. This is often referred to as hidden dependencies. This paper proposes a methodology to make these dependencies explicit for the declarative process modeling language Declare by considering a Declare model as a graph and relying on the constraints’ characteristics. Moreover, this paper also contributes by empirically confirming that a tool that can visualize hidden dependency information on top of a Declare model has a significant positive impact on the understandability of Declare models.

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Improving Understandability of Declarative Process Models by Revealing Hidden Dependencies

In management theory of the last decades, much importance has been attached to a process-oriented perspective on organizational (re)structuring. Yet to date, organi-zations still experience difficulties in applying this process-oriented perspective to the design and maintenance information systems. The root of the problem lies with a procedural representation of business processes that contains inadequate information for computer systems to provide flexible automated business process support. The counter-part of a procedural representation is a declarative one that explicitly takes into account the business concerns that govern business processes. Recently, a number of process modeling languages have appeared that could be identified as declarative languages. These modeling languages have very distinct knowledge representation backgrounds, often lack a formal execution model and often only model one aspect of the many business concerns that exist in reality. What is needed are meaningful ways to combine several kinds of expressions, called business rule types, independently of the used methods for knowledge representation and reasoning. In this paper, we present the EM-BrA2CE (Enterprise Modeling using Business Rules, Agents, Activities, Concepts and Events) Framework, a unifying vocabulary and execution model for declarative process modeling. The vocabulary is described in terms of the Semantics for Business Vocabulary and Rules (SBVR) standard and the execution model is presented as a Colored Petri Net (CP-Net). In addition, we show how declarative process models can contribute to the model-driven design of Service-Oriented Architectures.

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Jan Vanthienen

Papers

Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port

Bankruptcy prediction with least squares support vector machine classifiers

Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring

Improving Understandability of Declarative Process Models by Revealing Hidden Dependencies

EM-BrA2CE v0.1: A vocabulary and execution model for declarative business process modeling