Remote sensing data management and its use for classification and inference purposes is at the forefront of research tasks nowadays. There are, however, some inherent drawbacks and difficulties when dealing with, and understanding how satellite information is provided (particularly when referring to multiband/multispectral satellite platforms) and how different and disparate datasets related to soil content can be used and merged with this imagery.We present WALGREEN. The aim of this tool is to provide a secure environment to handle the whole process to use polygons or, geographical coordinates in tiff/geotiff images, have real-time access to images, save and get soil organic carbon real measurements, and generate datasets for machine learning training and inferential methods. We also aim to providing a framework to preprocess soil organic carbon information from different but accepted sources, like the Land Use/Cover Area frame statistical Survey database, so that even without real measurements, researchers may be able to start training different machine learning methodologies.

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.

This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.

One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an acceptable execution time. In this paper we present Approx-SMOTE, a parallel implementation of the SMOTE algorithm for the Apache Spark framework. The key difference with the original SMOTE, besides parallelism, is that it uses an approximated version of k-Nearest Neighbor which makes it highly scalable. Although an implementation of SMOTE for Big Data already exists (SMOTE-BD), it uses an exact Nearest Neighbor search, which does not make it entirely scalable. Approx-SMOTE on the other hand is able to achieve up to 30 times faster run times without sacrificing the improved classification performance offered by the original SMOTE.

A novel approach to prototype selection for multi-output regression data sets is presented. A multi-objective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction quality expressed in terms of root mean squared error. A multi-target regressor based on k-NN was used for that purpose during the training to evaluate the error, while the tests were performed using four different multi-target predictive models. The distance matrices used by the multi-target regressor were cached to accelerate operational performance. Multiple Pareto fronts were also used to prevent overfitting and to obtain a broader range of solutions, by using different probabilities in the initialization of populations and different evolutionary parameters in each one. The results obtained with the benchmark data sets showed that the proposed method greatly reduced data set size and, at the same time, improved the predictive capabilities of the multi-output regressors trained on the reduced data set.

Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing to find similarities between instances. While the complexity of conventional methods (usually quadratic, O(n2), or log-linear, O(nlogn)) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances).

Disturbing Neighbors (DN) is a method for generating classifier ensembles. Moreover, it can be combined with any other ensemble method, generally improving the results. This paper considers the application of these ensembles to imbalanced data: classification problems where the class proportions are significantly different. DN ensembles are compared and combined with Bagging, using three tree methods as base classifiers: conventional decision trees (C4.5), Hellinger distance decision trees (HDDT) -- a method designed for imbalance data -- and model trees (M5P) -- trees with linear models at the leaves -- . The methods are compared using two collections of imbalanced datasets, with 20 and 66 datasets, respectively. The best results are obtained combining Bagging and DN, using conventional decision trees.

In this paper we present a tool to assist in teaching top-down and bottom-up analysis algorithms. The tool provides simulation for the following analysis algorithms: LL, SLR, LALR and LR. During the simulation the student can simultaneously see the pending input, the analysis stack and tables, the generated output and the parse tree.

In this paper we present the new version of a tool to assist in teaching formal languages and automata theory. In the previous version the tool provided algorithms for regular expressions, finite automata and context free grammars. The new version can simulate as well push-down automata and Turing machines.

In this paper we propose a novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and diversity of individual classifiers. The proposed approach combines the philosophy of boosting, putting more effort on difficult instances, with the basis of the random subspace method. Our main contribution is that instead of using a random subspace, we construct a projection taking into account the instances which have posed most difficulties to previous classifiers. In this way, consecutive nonlinear projections are created by a neural network trained using only incorrectly classified instances. The feature subspace induced by the hidden layer of this network is used as the input space to a new classifier. The method is compared with bagging and boosting techniques, showing an improved performance on a large set of 44 problems from the UCI Machine Learning Repository. An additional study showed that the proposed approach is less sensitive to noise in the data than boosting methods.

The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM's structure. In this paper we propose a new way to perform that visualization using a variant of Andrews' Curves. Also we show that the interaction between these two methods allows us to find sub-clusters within identified clusters. Perhaps more importantly, using the SOM to pre-process data by identifying gross features enables us to use Andrews' Curves on data sets which would have previously been too large for the methodology. Finally we show how a three way interaction between the human user and these two methods can be a valuable exploratory data analysis tool.