Abstract
Different meta-heuristics (MHs) may find the best solutions for different traveling salesman problem (TSP) instances. The a priori selection of the best MH for a given instance is a difficult task. We address this task by using a meta-learning based approach, which ranks different MHs according to their expected performance. Our approach uses Multilayer Perceptrons (MLPs) for label ranking. It is tested on two different TSP scenarios, namely: re-visiting customers and visiting prospects. The experimental results show that: 1) MLPs can accurately predict MH rankings for TSP, 2) better TSP solutions can be obtained from a label ranking compared to multilabel classification approach, and 3) it is important to consider different TSP application scenarios when using meta-learning for MH selection.

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.

Abstract

Abstract
This chapter introduces the volume on Applications of Data Mining in E-Business and Finance. It discusses how application-specific issues can affect the development of a data mining project. An overview of the chapters in the book is then given to guide the reader.

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Abstract
We investigate the problem of using past performance information to select an algorithm for a given classification problem. We present three ranking methods for that purpose: average ranks, success rate ratios and significant wins. We also analyze the problem of evaluating and comparing these methods. The evaluation technique used is based on a leave-one-out procedure. On each iteration, the method generates a ranking using the results obtained by the algorithms on the training datasets. This ranking is then evaluated by calculating its distance from the ideal ranking built using the performance information on the test dataset. The distance measure adopted here, average correlation, is based on Spearman's rank correlation coefficient. To compare ranking methods, a combination of Friedman's test and Dunn's multiple comparison procedure is adopted. When applied to the methods presented here, these tests indicate that the success rate ratios and average ranks methods perform better than significant wins.