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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

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Learning from imbalanced data: open challenges and future directions

Simulation and the monte carlo method

The paper presents the novel adaptive splitting and selection algorithm (AdaSS) used for learning compound pattern recognition system. Splitting a feature space into its constituents and selection of the best area classifier from the pool of available recognizers for each region are key processes of the proposed model. Both take place simultaneously as part of a compound optimization process aimed at maximizing system performance. Evolutionary algorithms are used to find out the optimal solution. The results of experiments for algorithm evaluation purposes prove the quality of the proposed approach.

Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas

Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

The paper presents a concept drift detection method for unsupervised learning which takes into consideration the prior knowledge to select the most appro- priate classification model. The prior knowledge carries information about the data dis- tribution patterns that reflect different concepts, which may occur in the data stream. The presented method serves as a temporary solution for a classification system after a virtual concept drift and also provides additional information about the concept data distribution for adapting the classification model. Presented detector uses a developed method called simulated recurrence and detector ensembles based on statistical tests. Evaluation is performed on benchmark datasets.

/pdf/concept-drift-detection-and-model-selection-with-simulated-1d19ttd6v3.pdf

Concept Drift Detection and Model Selection with Simulated Recurrence and Ensembles of Statistical Detectors

Editorial: Guest Editorial: Hybrid intelligent fusion systems

The traditional pattern recognition method assumes that the model that is used does not depend on data timing. This assumption is correct for several practical issues but it is not valid for the case where new data frequently becomes available (the socalled data streams). We could meet the above-mentioned situation in many practical issues, as spam filtering, intrusion detection/prevention (IDS/IPS) or recognition of client behaviour to enumerate only a few. In these cases, the dependencies between the observation and classes are continually changing. Unfortunately, most pattern recognition methods do not take the so-called concept drift into consideration and they cannot adapt to a new model. Therefore, design classifiers for data streams are currently the focus of intense research. Another important issue related to the recognition of data streams is the problem of data labelling. Traditional machine learning methods use supervised learning algorithms, which could produce a classifier on the basis of a set of labelled examples. In this approach, we should take into consideration the cost of data labelling, which is usually passed over. Let us notice that labels are usually assigned by human experts and therefore they cannot label all new examples if they come too fast. Therefore, methods of classifier design which could produce the recognition system on the basis of a partially labelled set of examples (called active learning) would be an attractive proposition. This article focuses on the problem of the concept drift using active learning approach for the minimal distance classifiers. The potential for adaptation of the proposed method and its quality are evaluated through computer experiments, carried out on several benchmark data sets.

Michal Wozniak

Papers

Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas

Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

Concept Drift Detection and Model Selection with Simulated Recurrence and Ensembles of Statistical Detectors

Editorial: Guest Editorial: Hybrid intelligent fusion systems

Active learning approach to concept drift problem