Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

This book presents state-of-the-art developments in the area of computationally intelligent methods applied to various aspects and ways of Web exploration and Web mining. Some novel data mining algorithms that can lead to more effective and intelligent Web-based systems are also described. Scientists, engineers, and research students can expect to find many inspiring ideas in this volume.

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.

Decision tree learning is one of the main methods of learning from data. It has been applied to a variety of different domains over the past three decades. In the real world, accuracy is not enough; there are costs involved, those of obtaining the data and those when classification errors occur. A comprehensive survey of cost-sensitive decision tree learning has identified over 50 algorithms, developing a taxonomy in order to classify the algorithms by the way in which cost has been incorporated, and a recent comparison shows that many cost-sensitive algorithms can process balanced, two class datasets well, but produce lower accuracy rates in order to achieve lower costs when the dataset is less balanced or has multiple classes. This thesis develops a new framework and algorithm concentrating on the view that cost-sensitive decision tree learning involves a trade-off between costs and accuracy. Decisions arising from these two viewpoints can often be incompatible resulting in the reduction of the accuracy rates. The new framework builds on a specific Game Theory problem known as the multi-armed bandit. This problem concerns a scenario whereby exploration and exploitation are required to solve it. For example, a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to pay out the most. Game Theory proposes a solution of this problem which is solved by a process of exploration and exploitation in which reward is maximized. This thesis utilizes these concepts from the multi-armed bandit game to develop a new algorithm by viewing the rewards as a reduction in costs, utilizing the exploration and exploitation techniques so that a compromise between decisions based on accuracy and decisions based on costs can be found. The algorithm employs the adapted multi-armed bandit game to select the attributes during decision tree induction, using a look-ahead methodology to explore potential attributes and exploit the attributes which maximizes the reward. The new algorithm is evaluated on fifteen datasets and compared to six well-known algorithms J48, EG2, MetaCost, AdaCostM1, ICET and ACT. The results obtained show that the new multi-armed based algorithm can produce more cost-effective trees without compromising accuracy. The thesis also includes a critical appraisal of the limitations of the developed algorithm and proposes avenues for further research.