Abstract

Abstract
With so much information available on the web, looking for relevant documents on the Internet has become a difficult task. Temporal features play an important role with the introduction of a time dimension and the possibility to restrict a search by time, recreating a particular moment of a web page set. Despite its importance, temporal information is still under-considered by current search engines, limiting themselves to the capture of the most recent snapshot of the information. In this paper, we describe the architecture of a temporal search engine which uses timelines to browse search results. More specifically, we intend to add a time measure to cluster web page results, by analyzing web page contents, supporting the search of temporal and non-temporal information embedded in web documents.

Abstract

Abstract
Cost sensitive prediction is a key task in many real world applications. Most existing research in this area deals with classification problems. This paper addresses a related regression problem: the prediction of rare extreme values of a continuous variable. These values are often regarded as outliers and removed from posterior analysis. However, for many applications (e.g. in finance, meteorology, biology, etc.) these are the key values that we want to accurately predict. Any learning method obtains models by optimizing some preference criteria. In this paper we propose new evaluation criteria that are more adequate for these applications. We describe a generalization for regression of the concepts of precision and recall often used in classification. Using these new evaluation metrics we are able to focus the evaluation of predictive models on the cases that really matter for these applications. Our experiments indicate the advantages of the use of these new measures when comparing predictive models in the context of our target applications.

Abstract

Abstract
Purpose - The purpose of this paper is to investigate the applicability of meta-learning to the problem of algorithm recommendation for gene expression data classification. Design/methodology/approach - Meta-learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k-nearest neighbors and support vector machine-based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta-learning literature. Findings - Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed. Practical implications - As the experiments conducted in this paper suggest, the use of meta-learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification. Originality/value - This paper reports contributions to the areas of meta-learning and gene expression data analysis. Regarding the former, it supports the claim that meta-learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks. © Emerald Group Publishing Limited.