Abstract With millions of opinions written every day around the internet, analyzing review sentiment has been shown to be an interesting and relevant problem. Support vector machines offer an excellent alternative when the amount of available data makes other models, such as deep learning, infeasible. A usual way to detect hidden sentiments in textual data is to address the mutual information through a corpus with a support vector machine or any other sophisticated classification algorithm. Approaches that are able to extract information from sequences of words, such as string kernels, have the potential for better performance. However, finding similarities can be difficult given the ample texts used to express opinions and the wide variety of vocabulary. To solve that problem, we suggest using clustering methods to automatically group words into categories based on a word vector, replacing the words in the dataset with their corresponding categories, and then using these categories to find mutual information in the text with support vector machines that use string kernels. This approach significantly reduces the token space and enhances the efficiency of the kernel methods. The proposed method showed better performance than state-of-the-art approaches for this task in a set of real-world problems. Different models were tested against our proposal. Results indicate that the proposed method has the ability to extract useful data from opinions in long texts and remains an interesting option for review sentiment analysis in general, even outperforming other state-of-the-art methods in certain datasets. It also opens the possibility of applying the same philosophy to deep learning and similar models.

Multilabel classification is a growing paradigm in the fields of data mining and machine learning. In multilabel learning, each input sample can belong to more than one binary class (termed “label”) in a multilabel framework, with contrasts with the standard single-label learning scenario. The challenge of producing a better classifier lies in the advantageous use of the correlations among different labels. In recent years, many multilabel models have been proposed, making it difficult to decide which methods to use. In this paper, we present the most comprehensive ranking performance comparison carried out thus far among numerous methods. We conduct a comprehensive analysis by comparing several configurations of 56 distinct methods, resulting in a total of 173 trained models. In addition, we utilize an extensive collection of problems involving 65 datasets. Furthermore, we analyze the effectiveness of the tested techniques by evaluating their performance with six different ranking performance metrics. Our findings indicate that while certain strategies consistently rank highly among the top-performing models, the most effective methods are strongly correlated with the specific metrics used to assess their performance. Finally, we examine many behavioral aspects of the different approaches.

Multilabel classification as a data mining task has recently attracted increasing interest from researchers. Many current data mining applications address problems with instances that belong to more than one class. These problems require the development of new, efficient methods. Advantageously using the correlation among different labels can provide better performance than methods that manage each label separately. In recent decades, many methods have been developed to deal with multilabel datasets, which makes it difficult to decide which method is the most appropriate for a given task. In this paper, we present the most comprehensive comparison carried out so far. We compare a total of 62 different methods and several configurations of each one for a total of 197 trained models. We also use a large set of problems comprising 65 datasets. In addition, we studied the efficiency of the methods considering six different classification performance metrics. Our results show that, although there are methods that repeatedly appear among the top-performing models, the best methods are closely related to the metric used for evaluating the performance. We also analyzed different aspects of the behavior of the methods.

Feature selection is one of the most frequent tasks in data mining applications. Its ability to remove useless and redundant features improves the classification performance and gains knowledge about a given problem makes feature selection a common first step in data mining. In many feature selection applications, we need to combine the results of different feature selection processes. The two most common scenarios are the ensembles of feature selectors and the scaling up of feature selection methods using a data division approach. The standard procedure is to store the number of times every feature has been selected as a vote for the feature and then evaluate different selection thresholds with a certain criterion to obtain the final subset of selected features. However, this method is suboptimal as the relationships of the features are not considered in the voting process. Two redundant features may be selected a similar number of times due to the different sets of instances used each time. Thus, a voting scheme would tend to select both of them. In this article, we present a new approach: instead of using only the number of times a feature has been selected, the approach considers how many times the features have been selected together by a feature selection algorithm. The proposal is based on constructing an undirected graph where the vertices are the features, and the edges count the number of times every pair of instances has been selected together. This graph is used to select the best subset of features, avoiding the redundancy introduced by the voting scheme. The proposal improves the results of the standard voting scheme in both ensembles of feature selectors and data division methods for scaling up feature selection.

Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. The best approaches use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this problem with the best possible performance. A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper we present a methodology for combining many sources of information to recognize any functional site using "floating search", a powerful heuristics applicable when the cost of evaluating each solution is high. We present experiments on four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods. The results show an advantage of the proposed method and also challenge the standard assumption of using only genomes not very close and not very far from the human to improve the recognition of functional sites.

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly produced in many fields of research. At the same time, most of the recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of variables significantly harms classification or recognition tasks. There are efficiency issues, too, because the speed of many classification algorithms is largely improved when the complexity of the data is reduced. One of the approaches to address problems that have too many features or instances is feature or instance selection, respectively. Although most methods address instance and feature selection separately, both problems are interwoven, and benefits are expected from facing these two tasks jointly. This paper proposes a new memetic algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection. The proposed method performs four different local search procedures with the aim of obtaining the most relevant subsets of instances and features to perform an accurate classification. A new fitness function is also proposed that enforces instance selection but avoids putting too much pressure on removing features. We prove experimentally that this fitness function improves the results in terms of testing error. Regarding the scalability of the method, an extension of the stratification approach is developed for simultaneous instance and feature selection. This extension allows the application of the proposed algorithm to large datasets. An extensive comparison using 55 medium to large datasets from the UCI Machine Learning Repository shows the usefulness of our method. Additionally, the method is applied to 30 large problems, with very good results. The accuracy of the method for class-imbalanced problems in a set of 40 datasets is shown. The usefulness of the method is also tested using decision trees and support vector machines as classification methods.

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

Instance selection is becoming more and more relevant due to the huge amount of data that is constantly being produced. Research areas such as bioinformatics, text mining and intrusion detection, are generating huge amounts of information that must be dealt with. Instance selection is a powerful tool to reduce that information to manageable datasets. Most of the datasets in these areas shares a common property, they are heavily class-imbalanced. The class of interest, or positive or minority class, is outnumbered many times by the negative, or majority, class. Thus, any instance selection algorithm addressing these problems must take into account two important features of such problems. Firstly, the large size of the datasets that makes scalability issues very relevant. Secondly, the class-imbalanced distribution of the instances. In this paper, we propose a new methodology for instance selection that it is specifically designed for large class-imbalanced datasets. We use a divide-and-conquer approach to deal with the scalability of the algorithms, and a combination of different rounds of instance selection to improve the results in terms of class-imbalance error measures. The validity of the proposed framework is assured using 45 datasets. Our proposal improves the results of standard methods in accuracy and storage reduction, and at the same time is able to reduce the time needed by the algorithms with a time complexity O(n log(n)).