Convolutional neural networks have been effective in several applications, arising as a promising supporting tool in a relevant Dermatology problem: skin cancer diagnosis. However, generalizing well can be difficult when little training data is available. The fine-tuning transfer learning strategy has been employed to differentiate properly malignant from non-malignant lesions in dermoscopic images. Fine-tuning a pre-trained network allows one to classify data in the target domain, occasionally with few images, using knowledge acquired in another domain. This work proposes eight fine-tuning settings based on convolutional networks previously trained on ImageNet that can be employed mainly in limited data samples to reduce overfitting risk. They differ on the architecture, the learning rate and the number of unfrozen layer blocks. We evaluated the settings in two public datasets with 104 and 200 dermoscopic images. By finding competitive configurations in small datasets, this paper illustrates that deep learning can be effective if one has only a few dozen malignant and non-malignant lesion images to study and differentiate in Dermatology. The proposal is also flexible and potentially useful for other domains. In fact, it performed satisfactorily in an assessment conducted in a larger dataset with 746 computerized tomographic images associated with the coronavirus disease.

Feature selection in real-world data mining problems is essential to make the learning task efficient and more accurate. Identifying the best feature selection algorithm, among the many available, is a complex activity that still relies heavily on human experts or some random trial-and-error procedure. Thus, the automated machine learning community has taken some steps towards the automation of this process. In this paper, we address the metalearning challenge of recommending feature selection algorithms by proposing a novel meta-feature engineering model. Our model considers a broad collection of meta-features that enable the study of the relationship between the dataset properties and the feature selection algorithm performance in terms of several criteria. We arrange the input meta-features into eight categories: (i) simple, (ii) statistical, (iii) information-theoretical, (iv) complexity, (v) landmarking, (vi) based on symbolic models, (vii) based on images, and (viii) based on complex networks (graphs). The target meta-features emerge from a multi-criteria performance measure, based on five individual performance indexes, that assesses feature selection methods grounded in information, distance, dependence, consistency, and precision measures. We evaluate our proposal using a recently developed framework that extracts the input meta-features from 213 benchmark datasets, and ranks the assessed feature selection algorithms, to fill in the target meta-features in meta-bases. This evaluation uses five state-of-the-art classification methods to induce recommendation models from meta-bases: C4.5, Random Forest, XGBoost, ANN, and SVM. The results showed that it is possible to reach an average accuracy of up to 90% applying our meta-feature engineering model. This work is the first to use an extensive empirical evaluation to provide a careful discussion of the strengths and limitations of more than 160 meta-features. These meta-features, while designed to aid the task of feature selection algorithm recommendation, can readily be employed in other metalearning scenarios. Therefore, we believe our findings are a valuable contribution to the fields of automated machine learning and data mining, as well as to the feature extraction and pattern recognition communities.

Each example in a multi-label dataset is associated with multiple labels, which are often correlated. Learning from this data can be improved when dimensionality reduction tasks, such as feature selection, are applied. The standard approach for multi-label feature selection transforms the multi-label dataset into single-label datasets before using traditional feature selection algorithms. However, this approach often ignores label dependence. In this work, we propose an alternative method, LCFS, that constructs new labels based on relations between the original labels. By doing so, the label set from the data is augmented with second-order information before applying the standard approach. To assess LCFS, an experimental evaluation using Information Gain as a measure to estimate the importance of features was carried out on 10 benchmark multi-label datasets. This evaluation compared four LCFS settings with the standard approach, using random feature selection as a reference. For each dataset, the performance of a feature selection method is estimated by the quality of the classifiers built from the data described by the features selected by the method. The results show that a simple LCFS setting gave rise to classifiers similar to, or better than, the ones built using the standard approach. Furthermore, this work also pioneers the use of the systematic review method to survey the related work on multi-label feature selection. The summary of the 99 papers found promotes the idea that exploring label dependence during feature selection can lead to good results.

Most supervised learning methods consider that each dataset instance is associated with a unique label. However, there are several domains in which the instances are associated with a set of labels (a multi-label). An alternative to investigate properties of multi-label data and their relationship with the learning performance consists in exploratory data analysis. This approach aims to obtain a better understanding of the data by using different techniques, most of them related to graphic representations. This work proposes ML-EDA, a framework for multi-label exploratory data analysis, which is publicly available in the Internet. The framework has been designed considering extensibility and maintainability as its main goals. Moreover, ML-EDA can directly process, among others, the information provided by MULAN, a framework for multi-label learning frequently used by the community. Some of the ML-EDA facilities are illustrated using benchmark multi-label datasets, highlighting its use as an additional resource to investigate multi-label data.

Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing the complexity of gene expression data sets for cancer diagnosis. We then investigate how these measures relate to the classification performances achieved by support vector machines, a popular Machine Learning technique usually employed in the analysis of gene expression data. The results obtained indicate that some of the complexity indices utilized are indeed successful in explaining the difficulty involved in the classification of cancer gene expression data.