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Contrast patterns, which lie in the core of most understandable classifiers, are frequently evaluated by quality measures. Since many different quality measures are available, they should be compared to select the most appropriate for each applications. This paper introduces a method to compare quality measures, using a set of mined patterns and a collection of objects not used for mining. The comparison is performed by correlating quality values with a quality estimation of the patterns. Additionally, a meta-learning study is performed to show that combining quality measures could be better than using the best single measures in isolation. The results of this paper can help researchers to create new quality measures or to find new combinations of quality measures to create better understandable classification systems. [ABSTRACT FROM AUTHOR]

Contrast pattern-based classifiers are an important family of both understandable and accurate classifiers. Nevertheless, these classifiers do not achieve good performance on class imbalance problems. In this paper, we introduce a new contrast pattern-based classifier for class imbalance problems. Our proposal for solving the class imbalance problem combines the support of the patterns with the class imbalance level at the classification stage of the classifier. From our experimental results, using highly imbalanced databases, we can conclude that our proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems. Additionally, we show that our classifier significantly outperforms other state-of-the-art classifiers not directly based on contrast patterns, which are also designed to deal with class imbalance problems. [ABSTRACT FROM AUTHOR]

• A systematic evaluation of filter Unsupervised Feature Selection methods is presented. • The most popular and recent filter UFS methods are included in our study. • The evaluation of the filter UFS methods followed the standards in the literature. • A general discussion based on the results of the evaluated methods is provided. • Some guidelines for the use of the evaluated filter UFS methods is also provided. Unsupervised Feature Selection (UFS) has aroused great interest in the last years because of its practical significance and application on a large variety of problems in expert and intelligent systems where unlabeled data appear. Specifically, Unsupervised Feature Selection methods based on the filter approach have received more attention due to their efficiency, scalability, and simplicity. However, in the literature, there are no comprehensive studies for assessing such UFS methods when they are applied, under the same conditions, to a wide variety of real-world data. To fill this gap, in this paper, we present a comprehensive empirical and systematic evaluation of the most popular and recent filter UFS methods, evaluating their performance in terms of clustering, classification, and runtime. The filter methods used in our study were applied on 50 datasets from the UCI Machine Learning Repository and 25 high dimensional datasets from the ASU Feature Selection Repository. To evaluate if the outcomes obtained by the assessed methods are statistically significant, the Friedman test and Holm post hoc procedure were applied in the clustering and classification results. From our experiments, we provide some practical guidelines and insights for the use of the filter UFS methods analyzed in our study. [ABSTRACT FROM AUTHOR]

Highlights • Outlier detection is important for improving clustering results. • Over detection of outliers leads to information loss. • Our proposal reduces the number of over-detected outliers. • Experiments show that clustering quality can be improved, while runtime is reduced. Abstract Expert and intelligent systems use a variety of machine learning techniques to obtain and understand the information inherent in the data. Clustering is one of these techniques, which has become important and popular since it allows classifying an unlabeled dataset into clusters of similar objects. There are many clustering algorithms that have been proposed in the literature. From these algorithms, the Cross-Clustering algorithm is one of the most recent clustering algorithms for partial clustering (clustering where not necessarily all the objects are grouped into clusters), which has provided good results allowing estimating a suitable set of clusters, as well as eliminating outliers. However, this algorithm tends to eliminate too many objects as outliers, which leads to discard a lot of non-outlier objects. Additionally, the Cross-Clustering algorithms spends a lot of time evaluating several combinations of clusterings, trying to determine a suitable number of clusters. To overcome these problems, in this paper, an improved version of the Cross-Clustering algorithm (ICC) is proposed. ICC changes the clustering algorithm used for detecting outliers, as well as it modifies the way outliers are detected. Moreover, a stop criterion allowing to make a fast decision on the estimation of a suitable number of cluster, is also introduced. The performance of the improved Cross-Clustering algorithm is compared with the original algorithm on artificial and real datasets. Our results show that ICC improves the original algorithm and other state of the art clustering algorithms; in both, runtime and clustering quality. [ABSTRACT FROM AUTHOR]