Meta-learning recommendation of default hyper-parameter values for SVMs in classifications tasks

Machine learning algorithms have been investigated in several scenarios, one of them is the data classification. The predictive performance of the models induced by these algorithms is usually strongly affected by the values used for their hyper-parameters. Different approaches to define these values have been proposed, like the use of default values and optimization techniques. Although default values can result in models with good predictive performance, different implementations of the same machine learning algorithms use different default values, leading to models with clearly different predictive performance for the same dataset. Optimization techniques have been used to search for hyper-parameter values able to maximize the predictive performance of induced models for a given dataset, but with the drawback of a high computational cost. A compromise is to use an optimization technique to search for values that are suitable for a wide spectrum of datasets. This paper investigates the use of meta-learning to recommend default values for the induction of Support Vector Machine models for a new classification dataset. We compare the default values suggested by the Weka and LibSVM tools with default values optimized by meta-heuristics on a large range of datasets. This study covers only classification task, but we believe that similar ideas could be used in other related tasks. According to the experimental results, meta-models can accurately predict whether tool suggested or optimized default values should be used.CAPESCNPqSão Paulo Research Foundation (FAPESP) (grant#2012/23114-9

Visual Identification of Inconsistency in Pattern

The visual identification of inconsistencies in patterns is an area in computing that has been understudied. While pattern visualisation exposes the relationships among identified regularities, it is still very important to identify inconsistencies (irregularities) in identified patterns. The significance of identifying inconsistencies for example in the growth pattern of children of a particular age will enhance early intervention such as dietary modifications for stunted children. It is described in this chapter, the need to have a system that identifies inconsistencies in identified pattern of a dataset. Also, techniques that enable the visual identification of inconsistencies in patterns such as fault tolerance and colour coding are described. Two approaches are presented in this chapter for visualising inconsistencies in patterns namely; visualising inconsistencies in objects with many attribute values and visual comparison of an investigated dataset with a case control dataset. These approaches are associated with tools which were developed by the authors of this chapter: Firstly, ConTra which allows its users to mine and analyse the contradictions in attribute values whose data does not abide by the mutual exclusion rule of the dataset. Secondly, Datax which mines missing data; enables the visualisation of the missingness and the identification of the associated patterns. Finally, WellGrowth which explores Children’s growth dataset by comparing an investigated dataset (data obtained from a Primary Health Centre) with a case control dataset (data from the website of World Health Organisation). Instances of inconsistencies as discovered in the explored datasets are discussed