Diversity is a fundamental component in ensemble methods, crucial for enhancing the overall performance and robustness of predictive models. In bagging and boosting, diversity is implicitly generated through the data sampling process. In stacking, diversity is introduced by incorporating heterogeneous machine learning models as base learners, and an error function is created to focus on minimizing the meta-learner’s overall errors. Some models promote diversity directly within the error function; however, they often prioritize generating competitive and diverse individual learners, neglecting the necessity of creating an ensemble that is collectively accurate, as seen in stacking, whilst ensuring diversity among its members. Motivated by this point, two ensemble models are proposed in this manuscript, named Global and Diverse Ensemble Methods. These models incorporate implicit diversity through data, diversity in the heterogeneity of base learners, and an error function designed to produce a competitive overall ensemble with diverse individuals. The two diversity proposals included in the models are negative correlation and squared Pearson correlation. In both cases, the error function incorporates the minimization of the ensemble’s overall error (global error measure) in addition to promoting diversity. The proposed methods have been rigorously tested on 45 publicly available regression datasets using shallow base learners, as well as on 7 additional datasets using deep base learners, yielding very promising results. These findings underscore the importance of integrating these diversity-promoting elements and minimizing the global errors of the ensemble, rather than focusing solely on the errors of individual base learners, in the design of ensemble methods.

Reducing heat dissipation is critical for modern multi-core systems to meet increasing computational performance requirements. In this paper, we investigate the impact of idle-time distribution on the peak temperature of Multi-processor System-on-Chip (MPSoCs) for the constrained-deadline non-preemptive task scheduling problem that is common in safety-critical systems. It is assumed that the transient thermal behavior of the platform cannot be neglected and must be modeled and accounted for by the optimization algorithms. In this context, we derive a dual-node thermal model that can be well applied to a dual-cluster i.MX8 QuadMax from NXP. Based on this model, we implement two offline optimization-based strategies, including an iterative per-core approach based on the principles presented in the related literature and a novel holistic approach. The results show that the per-core approach and the holistic approach reduce the peak temperature by 7.1% and 14% on average compared to the traditional non-thermal approach. We perform the experiments on the i.MX8 QuadMax platform to validate the applicability of the results and observe a good match between the model-based simulations and the actual physical platform measurements.

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.

Ensembles are a widely implemented approach in the machine learning community and their success is traditionally attributed to the diversity within the ensemble. Most of these approaches foster diversity in the ensemble by data sampling or by modifying the structure of the constituent models. Despite this, there is a family of ensemble models in which diversity is explicitly promoted in the error function of the individuals. The negative correlation learning (NCL) ensemble framework is probably the most well-known algorithm within this group of methods. This article analyzes NCL and reveals that the framework actually minimizes the combination of errors of the individuals of the ensemble instead of minimizing the residuals of the final ensemble. We propose a novel ensemble framework, named global negative correlation learning (GNCL), which focuses on the optimization of the global ensemble instead of the individual fitness of its components. An analytical solution for the parameters of base regressors based on the NCL framework and the global error function proposed is also provided under the assumption of fixed basis functions (although the general framework could also be instantiated for neural networks with nonfixed basis functions). The proposed ensemble framework is evaluated by extensive experiments with regression and classification data sets. Comparisons with other state-of-the-art ensemble methods confirm that GNCL yields the best overall performance.

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.

On-chip power dissipation is recognized as one of the primary limiters, if not a show stopper, of performance for high-end safety-critical uniform multi-core processors. This paper proposes an efficient and simple thermal model for such a platform to be coupled with the large variety of schedulers designed to control the processor activity and the triggering of the cooling mechanism with as little impact on performance as possible.

Technology advances in microprocessor design have resulted in high device density and performance during the last decades. More components are fabricated on the chip die and millions, if not billions, of instructions can now be executed within microseconds. A consequence of this advancement is heat dissipation by the microprocessors. In this context, elevated on-chip temperature issues have become an important subject for the design of future generations of microprocessors, especially in avionics and automotive industries. In this paper, we address the scheduling problem of non-preemptive periodic tasks on a single processor platform under thermal-aware design. We assume that the tasks are scheduled by following any Fixed-Task-Priority (FTP) scheduler (e.g., Rate Monotonic (RM) or Deadline Monotonic (DM)) and we propose a unique framework wherein we capture both the temporal and thermal behavior of the system. Then, we present two new thermal-aware scheduling strategies, referred to as NP-HBC and NP-CBH, to keep the system temperature within specified parameters and we derive their respective schedulability analysis. Finally, we evaluate the performance of the proposed theoretical results through intensive simulations.

BACKGROUND: Recognizing the different functional parts of genes, such as promoters, translation initiation sites, donors, acceptors and stop codons, is a fundamental task of many current studies in Bioinformatics. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. However, with the rapid evolution of our ability to collect genomic information, it has been shown that combining many sources of evidence is fundamental to the success of any recognition task. With the advent of next-generation sequencing, the number of available genomes is increasing very rapidly. Thus, methods for making use of such large amounts of information are needed. RESULTS: In this paper, we present a methodology for combining tens or even hundreds of different classifiers for an improved performance. Our approach can include almost a limitless number of sources of evidence. We can use the evidence for the prediction of sites in a certain species, such as human, or other species as needed. This approach can be used for any of the functional recognition tasks cited above. However, to provide the necessary focus, we have tested our approach in two functional recognition tasks: translation initiation site and stop codon recognition. We have used the entire human genome as a target and another 20 species as sources of evidence and tested our method on five different human chromosomes. The proposed method achieves better accuracy than the best state-of-the-art method both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. Furthermore, our approach shows a more principled way for selecting the best genomes to be combined for a given recognition task. CONCLUSIONS: Our approach has proven to be a powerful tool for improving the performance of functional site recognition, and it is a useful method for combining many sources of evidence for any recognition task in Bioinformatics. The results also show that the common approach of heuristically choosing the species to be used as source of evidence can be improved because the best combinations of genomes for recognition were those not usually selected. Although the experiments were performed for translation initiation site and stop codon recognition, any other recognition task may benefit from our methodology.

MOTIVATION: The recognition of translation initiation sites and stop codons is a fundamental part of any gene recognition program. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. These methods all use two classes, one of positive instances and another one of negative instances that are constructed using sequences from the whole genome. However, the features of the negative sequences differ depending on the position of the negative samples in the gene. There are differences depending on whether they are from exons, introns, intergenic regions or any other functional part of the genome. Thus, the positive class is fairly homogeneous, as all its sequences come from the same part of the gene, but the negative class is composed of different instances. The classifier suffers from this problem. In this article, we propose the training of different classifiers with different negative, more homogeneous, classes and the combination of these classifiers for improved accuracy. RESULTS: The proposed method achieves better accuracy than the best state-of-the-art method, both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. The method is tested on the whole human genome. The results for recognizing both translation initiation sites and stop codons indicated improvements in the rates of both false-negative results (FN) and false-positive results (FP). On an average, for translation initiation site recognition, the false-negative ratio was reduced by 30.2% and the FP ratio decreased by 10.9%. For stop codon prediction, FP were reduced by 41.4% and FN by 31.7%. AVAILABILITY AND IMPLEMENTATION: The source code is licensed under the General Public License and is thus freely available. The datasets and source code can be obtained from http://cib.uco.es/site-recognition. CONTACT: [email protected].

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.

Gene structure prediction consists of determining which parts of a genomic sequence of the cell are coding, and constructing the whole gene from its start site to its stop codon. Gene recognition is one of the most important open problems in bioinformatics. The subtle sources of evidence and the many pitfalls of the problem make gene recognition in eukaryotes one of the most challenging tasks in this field. Gene recognition may be considered as a search problem, where many evidence sources are combined in a scoring function that must be maximized to obtain the structure of a probable gene. Using an intrinsic method, we propose a combination of evolutionary computation and support vector machines for gene structure prediction. Specifically, we use support vector machines (SVMs) to localize and score the functional sites along the genomic sequence, reducing the search space. Evolutionary computation is used to evolve a population where the individuals are correct gene structures. The flexibility of evolutionary computation can be used to account for the complexities of the problem, which are growing as our knowledge of the molecular processes of transcription and translation deepens. Our results show that with a very simple program we are able to achieve very good accuracies in the recognition of genes in human chromosome 19.