Index of balanced accuracy: a performance measure for skewed class distributions

This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning processes in two-class imbalanced domains. The method combines an unbiased index of its overall accuracy and a measure about how dominant is the class with the highest individual accuracy rate. Some theoretical examples are conducted to illustrate the benefits of the new metric over other well-known performance measures. Finally, a number of experiments demonstrate the consistency and validity of the evaluation method here propose

Fano resonances in THz metamaterials composed of continuous metallic wires and split ring resonators

Cataloged from PDF version of article.We demonstrate theoretically and experimentally that Fano resonances can be obtained in terahertz metamaterials that are composed of periodic continuous metallic wires dressed with periodic split ring resonators. An asymmetric Fano lineshape has been found in a narrow frequency range of the transmission curve. By using a transmission line combined with lumped element model, we are able to not only fit the transmission spectra of Fano resonance which is attributed to the coupling and interference between the transmission continuum of continuous metallic wires and the bright resonant mode of split ring resonators, but also reveal the capacitance change of the split ring resonators induced frequency shift of the Fano resonance. Therefore, the proposed theoretical model shows more capabilities than conventional coupled oscillator model in the design of Fano structures. The effective parameters of group refractive index of the Fano structure are retrieved, and a large group index more than 800 is obtained at the Fano resonance, which could be used for slow light devices. (C) 2014 Optical Society of Americ

Well-promising outcomes with vacuum-assisted closure in an infected wound following laparotomy: A case report

Introducation: Negative pressure wound therapy (NPWT) represents an alternative method to optimize conditions for wound healing. Delayed wound closure is a significant health problem, which is directly associated with pain and suffering from patient's aspect, as well with social and financial burden. Presentation of case: We report a case of vacuum-assisted wound therapy with hypertonic solution distillation and continuous negative pressure application, in an infected wound after laparotomy for incisional hernia reconstruction with mesh placement. Negative pressure was initiated at the wound margins after failure of conventional treatment with great outcomes, achieving a total closure of the incision within two weeks. Discussion: Each wound has particular characteristics which must be managed. Vacuum assisted closure (VAC) with continuous negative pressure and simultaneous wound instillation and cleanse can provide optimum results, reducing the cavity volume, by newly produced granulated tissue. Conclusion: The simultaneous use of instillation and constant pressure seemed to be superior in comparison with NPWT alone. Compared to conventional methods, the use of VAC ends to better outcomes, in cases of infected wounds following laparotomy

Use of ensemble based on GA for imbalance problem

In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. One method to tackle this problem consists to resample the original training set, either by over-sampling the minority class and/or under-sampling the majority class. In this paper, we propose two ensemble models (using a modular neural network and the nearest neighbor rule) trained on datasets under-sampled with genetic algorithms. Experiments with real datasets demonstrate the effectiveness of the methodology here propose

Exploring the performance of resampling strategies for the class imbalance problem

The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with the ratio between minority and majority classes. Experiments using eight different classifiers show that the most significant differences are for data sets with low or moderate imbalance: over-sampling clearly appears as better than under-sampling for local classifiers, whereas some under-sampling strategies outperform over-sampling when employing classifiers with global learning

Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the prediction of future glucose values in diabetes, which is a delicate task as unstable predictions can leave the patient in doubt and make him/her take the wrong action, threatening his/her life. The study is conducted on type 1 and type 2 diabetic people, with a focus on predictions made 30-minutes ahead of time. First, we confirm the superiority, in the context of glucose prediction, of the LSTM model by comparing it to other state-of-the-art models (Extreme Learning Machine, Gaussian Process regressor, Support Vector Regressor). Then, we show the importance of making stable predictions by smoothing the predictions made by the models, resulting in an overall improvement of the clinical acceptability of the models at the cost in a slight loss in prediction accuracy. Finally, we show that the proposed approach, outperforms all baseline results. More precisely, it trades a loss of 4.3\% in the prediction accuracy for an improvement of the clinical acceptability of 27.1\%. When compared to the moving average post-processing method, we show that the trade-off is more efficient with our approach

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Alternative organizing in times of crisis : resistance assemblages and socio-spatial solidarity

This paper draws on research conducted in Greece, where, during the last seven years, an acute socio-economic crisis has led to the emergence of a number of alternative organizational forms. By foregrounding the term drasis, the unexpected unfolding of an event in a specific space and time, we discuss how these alternative forms assemble differential capacities in order to resist the neoliberal ordering of socio-spatial and economic relations. In particular, we focus on two self-organized spaces, namely, a social centre and a squatted public garden and discuss two concrete instances of drasis. We propose that drasis instigates the establishment and evolution of transformative, prefigurative organizing through three interrelated processes, namely, the formation of resistance assemblages, social learning and socio-spatial solidarity. The paper offers three propositions, suggesting that drasis provides the socio-material conditions through which new resistance formations challenge the established productive forces of society and co-produce alternative forms of civic life.© 2017 published by SAGE. This is an author produced version of a paper published in European Urban and Regional Studies, uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at http://journals.sagepub.com/doi/10.1177/0969776416683001. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it