Page
%P
20 Result(s)
within
Dr. Alex A. Freitas
-
Chapter
Directions for Future Research on the Automatic Design of Data Mining Algorithms
Chapter 7 (entitled “Directions for Future Research on the Automatic Design of Data Mining Algorithms”) first summarizes the main contribution of the book, noting that the proposed genetic programming system ...
-
Chapter
Data Mining
Chapter 2 (entitled “Data Mining") provides a review of the data mining concepts and methods relevant for an understanding of the genetic programming system proposed in this book. The focus is on the classifi...
-
Chapter
Genetic Programming for Classification and Algorithm Design
Chapter 4 (entitled “Genetic Programming for Classification and Algorithm Design”), consists of two broad parts. The first part is about the classification task of data mining. In this first part, the chapter...
-
Chapter
Computational Results on the Automatic Design of Full Rule Induction Algorithms
Chapter 6 (entitled “Computational Results on the Automatic Design of Full Rule Induction Algorithms”), reports the results of extensive experiments performed to evaluate the proposed genetic programming syst...
-
Chapter
Introduction
Chapter 1 (entitled “Introduction”) consists of three parts. First, it briefly introduces the areas of data mining and evolutionary computation. Concerning data mining, the focus is on rule induction algorith...
-
Chapter
Evolutionary Algorithms
Chapter 3 (entitled “Evolutionary Algorithms”) provides a review of the evolutionary computation concepts and methods relevant for an understanding of the genetic programming system proposed in this book. Thi...
-
Chapter
Automating the Design of Rule Induction Algorithms
Chapter 5 (entitled “Automating the Design of Rule Induction Algorithms”) describes in detail the main contribution of this book, which is a grammar-based genetic programming system for automatically evolving...
-
Book
-
Chapter
Introduction
Nowadays there is a huge amount of data stored in real-world databases, and this amount continues to grow fast. As pointed out by [Piatetsky-Shapiro 1991], this creates both an opportunity and a need for (semi...
-
Chapter
Conclusions and Research Directions
This chapter is divided into two parts. Section 12.1 presents some general remarks on data mining with evolutionary algorithms (EAs). These remarks can be regarded as a very compact summary of the main argumen...
-
Chapter
Data Preparation
No matter how “intelligent” a data mining algorithm is, it will fail to discover high-quality knowledge if it is applied to low-quality data. In this chapter we focus on data preparation methods for data minin...
-
Chapter
Genetic Algorithms for Rule Discovery
In this chapter we discuss several issues related to develo** genetic algorithms (GAs) for prediction-rule discovery. The development of a GA for rule discovery involves a number of nontrivial design decisio...
-
Chapter
Evolutionary Algorithms for Clustering
In section 2.4 we reviewed the basic ideas of two major types of clustering methods, namely iterative-partitioning and hierarchical methods. In this chapter we discuss several issues in the development of Evo...
-
Chapter
Scaling up Evolutionary Algorithms for Large Data Sets
One well-known disadvantage of evolutionary algorithms (EAs) for rule discovery is that in general they are slow, by comparison with rule discovery algorithms based on the rule induction paradigm. After all, r...
-
Chapter
Data Mining Paradigms
As mentioned in the Introduction, since data mining is a very interdisciplinary field, there are many different paradigms of data mining algorithms, such as decision-tree building, rule induction, instance-bas...
-
Chapter
Basic Concepts of Evolutionary Algorithms
This chapter discusses some basic concepts and principles of Evolutionary Algorithms (EAs), focusing mainly on Genetic Algorithms (GAs) and Genetic Programming (GP). The main goal of this chapter is to help th...
-
Chapter
Genetic Programming for Rule Discovery
In subsection 5.4.4 we saw that standard Genetic Programming (GP) for symbolic regression — where all terminals are real-valued variables or constants and all functions have real-valued inputs and output — can...
-
Chapter
Evolutionary Algorithms for Data Preparation
Clearly the quality of discovered knowledge strongly depends on the quality of the data being mined. This has motivated the development of several algorithms for data preparation tasks, as discussed in chapter 4.
-
Chapter
Evolutionary Algorithms for Discovering Fuzzy Rules
This chapter discusses several concepts and issues in the development of Evolutionary Algorithms (EAs) for discovering fuzzy prediction rules. We start with a review of basic concepts of fuzzy sets, in section...
-
Chapter
Data Mining Tasks and Concepts
There are several data mining tasks. Each task can be considered as a kind of problem to be solved by a data mining algorithm. Therefore, each task has its own requirements, and the kind of knowledge discovere...
