There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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.

Data Mining Practical Machine Learning Tools and Techniques

a b s t r a c t The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1374_2011-Derrac-SWEVO.pdf

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

In this paper we present a Gene Expression Programming algorithm for multi-label classification. This algorithm encodes each individual into a discriminant function that shows whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. Our proposal has been compared with some recently published algorithms. The results on several datasets demonstrate the feasibility of this approach to tackle with multi-label problems.

A niching algorithm to learn discriminant functions with multi-label patterns

The use of data mining techniques in educational domains helps to find new knowledge about how students learn and how to improve the resources management. Using these techniques for predicting school failure is very useful in order to carry out actions to avoid drop out. With this purpose, we try to determine the earliest stage when the quality of the results allows for clarifying the possibility of school failure. We process real information from a Spanish high school by structuring the whole data in incremental datasets, which represent how students' academic records grow. Our study reveals an early and robust detection of the risky cases of school failure at the end of the first out of four courses.

Discovering clues to avoid middle school failure at early stages

In this chapter different quality measures to evaluate the interest of the patterns discovered in the mining process are described. Patterns represent major features of data so their interestingness should be accordingly quantified by considering metrics that determine how representative a specific pattern is for the dataset. Nevertheless, a pattern can also be of interest for a user despite the fact that this pattern does not describe useful and intrinsic properties of data. Thus, any quality measure can be divided into two main groups: objective and subjective quality measures. Whereas objective measures describe statistical properties of data, subjective quality measures take into account both the data properties and external knowledge provided by the expert in the application domain.

Quality Measures in Pattern Mining

Instance-based classification algorithms perform their main learning process at the instance level. They try to approximate a function that assigns class labels to instances. The instance classifier is combined with an underlying MI assumption, which links the class label of instances inside a bag with the bag class label. Many strategies have been devised to construct the instance classifier. We discuss the most prominent of them: wrapper methods (Sect. 4.2), maximum likelihood methods (Sect. 4.3), decision trees and rules methods (Sect. 4.4), maximum margin methods (Sect. 4.5), connectionist methods (Sect. 4.6), and evolutionary methods (Sect. 4.7). An experimental analysis on the performance of representative instance-based classifiers is presented in Sect. 4.8. Summarizing remarks are given in Sect. 4.9.

Instance-Based Classification Methods

Component identification is a critical phase in software architecture analysis to prevent later errors and control the project time and budget. Obtaining the most appropriate architecture according to predetermined design criteria can be treated as an optimization problem, especially since the appearance of the Search Based Software Engineering, and its combination with bio-inspired metaheuristics. In this work, an evolutionary programming (EP) algorithm is used to identify components, based on a novel and comprehensible representation of software architectures.

Sebastián Ventura

Papers

A niching algorithm to learn discriminant functions with multi-label patterns

Discovering clues to avoid middle school failure at early stages

Quality Measures in Pattern Mining

Instance-Based Classification Methods

A novel component identification approach using evolutionary programming