Imbalanced Learning Based on Logistic Discrimination

In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its performance. To fully consider the class imbalance, we design a new cost function which takes into account the accuracies of both positive class and negative class as well as the precision of positive class. Unlike traditional logistic discrimination, the proposed method learns its parameters by maximizing the proposed cost function. Experimental results show that, compared with other state-of-the-art methods, the proposed one shows significantly better performance on measures of recall, g-mean, f-measure, AUC, and accuracy

A fast response & recovery H2S gas sensor based on α-Fe2O3 nanoparticles with ppb level detection limit

H2S gas sensor based on α-Fe2O3 nanoparticles was fabricated by post-thermal annealing of Fe3O4 precursor which was synthesized using a facile hydrothermal route. The characteristic techniques including X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were adopted to characterize the chemical composition and microstructure of the obtained samples. Gas-sensing performance of the sensor was investigated at different operation temperatures from 100 °C to 400 °C. Results showed that the sensor exhibited the best sensitivity, reproducibility and long-term stability for detecting H2S gas at an operating temperature of 300 °C. The detection limit towards H2S gas was 0.05 ppm, and the response time and recovery time was 30 s and 5 s, respectively. In addition, sensing mechanism of the sensor towards H2S was discussed