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

Customer churn prediction using data mining is an increasingly important concern in the highly saturated telecommunication sector. Albeit its popularity in research, few studies investigated the use of heterogeneous ensembles for this purpose. Therefore, this study evaluated and compared the performance of five grid-searched optimized base classifiers (logistic regression, decision trees, K-nearest neighbors, multilayer perceptron, and support vector machines) and their heterogeneous ensembles (stacking, grading, majority voting, weighted majority voting, and soft voting) on four different telecom datasets. The results indicate that there are significant improvements when using heterogeneous ensembles compared to single classifiers, with stacking being the most performant ensemble. The best meta-classifier for stacking was found to be a multilayer perceptron. Additionally, we identified that using probabilities as an input to the ensemble’s meta-classifier, such as in soft voting and stacking variants, can increase their performance.

Benchmarking Stacking Against Other Heterogeneous Ensembles in Telecom Churn Prediction

E-mail, SMS en andere nieuwe communicatievormen zijn een mooi voorbeeld van hoe het kan misgaan indien er niet voldoende aandacht is voor een gezonde informatiecultuur. Niets zo gemakkelijk en snel als een mailtje of een berichtje. Geen wachttijd en nagenoeg geen kosten. De cijfers liegen er niet om. Het aantal inkomende mails stijgt elk jaar met 30%, we besteden er uren per dag aan en het aantal gearchiveerde e-mails begint astronomische vormen aan te nemen. Daarnaast is er nog een hoop spam, maar gelukkig zien we die zelden als de spamfilters hun werk goed doen.

https://lirias.kuleuven.be/bitstream/123456789/449245/2/Vanthienen_CB_SD_GD.pdf

Informatie en communicatie zijn gratis, maar een ongezonde informatiecultuur kost handenvol geld

Intelligent information retrieval tools can help intelligence and security agencies to retrieve and exploit relevant information from unstructured information sources and give them insight into the criminal behavior and networks, in order to fight crime more efficiently and effectively. This article aims at analysing off-the-shelf information extraction tools on their applicability and competency for such applications.

Intelligent information retrieval tools for police

PurposeThis paper aims that digital transformation (DT) is crucial for companies to stay competitive. While research on DT has quickly gained great popularity, the intersection of trade associations (TAs) and their role in the DT of their members is not yet researched.Design/methodology/approachIn this paper, the authors conducted 20 interviews with Belgian TAs to investigate the role of a TAs in the DT of its members, and how they drive the DT of its members. In addition, the authors investigate the core tasks of TAs, the need of the different industries to digitalize, and the digital projects the different industries are working on.FindingsThe findings indicate that TAs can be in a prime position to steer the DT of their members, especially for industries comprised of smaller players. Their roles can range from informing roles to true leaders of DT by creating novel products, such as online platforms and driving the entire sector forwards.Research limitations/implicationsThese findings call for more research into TAs and how their role can be optimized for steering DT of their members.Originality/valueThis is the first study to extensively study the role of TAs on the DT of their members.

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Jan Vanthienen

Papers

Benchmarking Stacking Against Other Heterogeneous Ensembles in Telecom Churn Prediction

Informatie en communicatie zijn gratis, maar een ongezonde informatiecultuur kost handenvol geld

Intelligent information retrieval tools for police

The role of trade associations in the digital transformation of their industry