Bogdan Gabrys

Bio: Bogdan Gabrys is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 30, co-authored 182 publications receiving 5800 citations. Previous affiliations of Bogdan Gabrys include University of the West of Scotland & Alpen-Adria-Universität Klagenfurt.

An Overview of Classifier Fusion Methods

Abstract: A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided. 1. INTRODUCTION The objective of all decision support systems (DSS) is to create a model, which given a minimum amount of input data/information, is able to produce correct decisions. Quite often, especially in safety critical systems, the correctness of the decisions taken is of crucial importance. In such cases the minimum information constraint is not that important as long as the derivation of the final decision is obtained in a reasonable time. According to one approach, the progress of DSS should be based on continuous development of existing methods as well as discovering new ones. Another approach suggests that as the limits of the existing individual method are approached and it is hard to develop a better one, the solution of the problem might be just to combine existing well performing methods, hoping that better results will be achieved. Such fusion of information seems to be worth applying in terms of uncertainty reduction. Each of individual methods produces some errors, not mentioning that the input information might be corrupted and incomplete. However, different methods performing on different data should produce different errors, and assuming that all individual methods perform well, combination of such multiple experts should reduce overall classification error and as a consequence emphasise correct outputs. Information fusion techniques have been intensively investigated in recent years and their applicability for classification domain has been widely tested [1]-[14]. The problem arouse naturally as a need of improvement of classification rates obtained from individual classifiers. Fusion of data/information can be carried out on three levels of abstraction closely connected with the flow of the classification process: data level fusion, feature level fusion, and classifier fusion [15]. There is little theory about the first two levels of information fusion. However, there have been successful attempts to transform the numerical, interval and linguistic data into a single space of symmetric trapezoidal fuzzy numbers [14], [15], and some heuristic methods have been successfully used for feature level fusion [15]. A number of methods have been developed for classifier fusion also referred to as decision fusion or mixture of experts. Essentially, there are two general groups of classifier fusion techniques. The methods subjectively associated with the first group generally operate on classifiers and put an emphasis on a development of the classifier structure. They do not do anything with classifiers outputs until combination process finds single best classifier or a selected group of classifiers and only then their outputs are taken as a final decision or for further processing [2], [9], [10]. Another group of methods operate mainly on classifiers outputs, and effectively the combination of classifiers outputs is calculated [1], [3]-[8], [11]-[15]. The methods operating on classifiers outputs can be further divided according to the type of the output produced by individual classifiers. A diagrammatic representation of the proposed taxonomy of classifier fusion methods is shown in Figure 1.

Metalearning: a survey of trends and technologies

Abstract: Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

Machine learning

Abstract: 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.).

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Survey of clustering algorithms

Abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.