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Chapter and Conference Paper
Exploring the Reduction of Configuration Spaces of Workflows
Many current AutoML platforms include a very large space of alternatives (the configuration space) that make it difficult to identify the best alternative for a given dataset. In this paper we explore a method th...
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Chapter and Conference Paper
Symbolic Versus Deep Learning Techniques for Explainable Sentiment Analysis
Deep learning approaches have become popular in many different areas, including sentiment analysis (SA), because of their competitive performance. However, the downside of this approach is that they do not pro...
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Chapter
Concluding Remarks
As metaknowledge has a central role in many approaches discussed in this book, we address the issue of what kind of metaknowledge is used in different metalearning/AutoML tasks, such as algorithm selection, hy...
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Chapter
Metalearning for Hyperparameter Optimization
This chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperpar...
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Chapter
Setting Up Configuration Spaces and Experiments
This chapter discusses the issues relative to so-called configuration spaces that need to be set up before initiating the search for a solution. It starts by introducing some basic concepts, such as discrete a...
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Chapter and Conference Paper
On Usefulness of Outlier Elimination in Classification Tasks
Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our stu...
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Chapter
Algorithm Recommendation for Data Streams
This chapter focuses on metalearning approaches that have been applied to data streams. This is an important area, as many real-world data arrive in the form of a stream of observations. We first review some i...
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Chapter
Automating the Design of Complex Systems
This chapter discusses the issue of whether it is possible to automate the design of rather complex workflows needed when addressing more complex data science tasks. The focus here is on symbolic approaches, w...
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Chapter
Learning from Metadata in Repositories
This chapter describes the various types of experiments that can be done with the vast amount of data, stored in experiment databases. We focus on three types of experiments done with the data stored in OpenML.
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Chapter
Introduction
This chapter starts by describing the organization of the book, which consists of three parts. Part I discusses some basic concepts, including, for instance, what metalearning is and how it is related to autom...
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Chapter
Evaluating Recommendations of Metalearning/AutoML Systems
This chapter discusses some typical approaches that are commonly used to evaluate metalearning and AutoML systems. This helps us to establish whether we can trust the recommendations provided by a particular s...
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Chapter
Metalearning Approaches for Algorithm Selection II
This chapter discusses different types of metalearning models, including regression, classification and relative performance models. Regression models use a suitable regression algorithm, which is trained on t...
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Chapter
Automating Workflow/Pipeline Design
This chapter discusses the design of workflows (or pipelines), which represent solutions that involve more than one algorithm. This is motivated by the fact that many tasks require such solutions. This problem is...
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Chapter
Metalearning in Ensemble Methods
This chapter discusses some approaches that exploit metalearning methods in ensemble learning. It starts by presenting a set of issues, such as the ensemble method used, which affect the process of ensemble le...
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Chapter
Automating Data Science
It has been observed that, in data science, a great part of the effort usually goes into various preparatory steps that precede model-building. The aim of this chapter is to focus on some of these steps. A com...
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Chapter
Metadata Repositories
This chapter presents a review of online repositories where researchers can share data, code, and experiments. In particular, it covers OpenML, an online platform for sharing and organizing machine learning da...
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Chapter
Metalearning Approaches for Algorithm Selection I (Exploiting Rankings)
This chapter discusses an approach to the problem of algorithm selection, which exploits the performance metadata of algorithms (workflows) on prior tasks to generate recommendations for a given target dataset...
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Chapter
Dataset Characteristics (Metafeatures)
This chapter discusses dataset characteristics that play a crucial role in many metalearning systems. Typically, they help to restrict the search in a given configuration space. The basic characteristic of the...
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Article
Text documents streams with improved incremental similarity
There has been a significant effort by the research community to address the problem of providing methods to organize documentation, with the help of Information Retrieval methods. In this paper, we present se...
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Chapter and Conference Paper
Sentence Compression for Portuguese
The task of Sentence Compression aims at producing a shorter version of a given sentence. This task may assist many other applications, as Automatic Summarization and Text Simplification. In this paper, we inv...
