Search

The class imbalance problem is a challenge in supervised classification, since many classifiers are sensitive to class distribution, biasing their prediction towards the majority class. Usually, in imbalanced databases, contrast pattern miners extract a very large collection of patterns from the majority class but only a few patterns (or none) from the minority class. It causes that minority class objects have low support and they could be identified as noise and consequently discarded by the contrast pattern based classifier biasing the results towards the majority class. In the literature, the class imbalance problem is commonly faced by applying resampling methods. Therefore, in this paper, we present a study about the impact of using resampling methods for improving the performance of contrast pattern based classifiers in class imbalance problems. Experimental results using standard imbalanced databases show that there are statistically significant differences between using the classifier before and after applying resampling methods. Moreover, from this study, we provide a guide based on the class imbalance ratio for selecting a resampling method that jointly with a contrast pattern based classifier allows us to have good results in a class imbalance problem. [ABSTRACT FROM AUTHOR]

In supervised classification if one of the classes has fewer objects than the other, we have a class imbalance problem. One of the most common solutions to address class imbalance problems is oversampling, and SMOTE is the most referenced and well-known oversampling method. However, SMOTE creates synthetic objects in a random way, therefore it produces a different result each time it is applied, and in practice the user has to apply SMOTE several times for choosing the best of all the generated balanced datasets. For this reason, in this paper, we present SMOTE-D, a deterministic version of SMOTE, and propose new deterministic SMOTE-D-based versions of some of the most recent and successful SMOTE-based methods. In our experiments, we show that all proposed deterministic methods produce as good results as random methods but our proposals need to be applied just once. This is very important from a practical point of view since our proposals save time by avoiding multiple applications of them as SMOTE does and they provide one unique result. [ABSTRACT FROM AUTHOR]

In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel "Dynamic K" satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the "Dynamic K" satisfaction mechanism allow improving the performance of CAR-based classifiers. [ABSTRACT FROM AUTHOR]

Measuring the quality of a contrast pattern is an active and relevant area of pattern recognition and data mining. Quality measures are important tools in very different scenarios like supervised classification, pattern based clustering, and association rule mining. Consequently, and due to the large collection of available measures, it is important to perform comparative studies for each particular context. Most published studies comparing quality measures are theoretical and in the context of association rule evaluation. In this paper, we present an empirical comparison of the behavior of 33 quality measures in the context of supervised classification and contrast pattern filtering. A comprehensive experimentation using several databases compares the behavior of these measures in three different contexts: as aggregation value, as pattern evaluation for classification, and as pattern evaluation for filtering. Experiments also show that top-accurate quality measures for classification have a deceptive performance for pattern filtering, because they cannot distinguish among patterns with zero support in the negative class. [ABSTRACT FROM AUTHOR]

Abstract: Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature. [Copyright &y& Elsevier]

In this paper, an accurate classifier based on Class Association Rules (CARs), called CAR-NF, is proposed. CAR-NF introduces a new strategy for computing CARs, using the Netconf as measure of interest, that allows to prune the CAR search space for building specific rules with high Netconf. Moreover, we propose and prove a proposition that supports the use of a Netconf threshold value equal to 0.5 for mining the CARs. Additionally, a new way for ordering the set of CARs based on their rule sizes and Netconf values is introduced in CAR-NF. The ordering strategy together with the "Best K rules" satisfaction mechanism allows CAR-NF to have better accuracy than CBA, CMAR, CPAR, TFPC and HARMONY classifiers, the best classifiers based on CARs reported in the literature. [ABSTRACT FROM AUTHOR]