Ensemble with estimation : seeking for optimization in class noisy data

Class noise, as know as the mislabeled data in training set, can lead to poor accuracy in classification no matter what machine learning methods are used. A reasonable estimation of class noise has a significant impact on the performance of learning methods. However, the error in estimation is inevitable theoretically. In this work, we propose an ensemble with estimation method to overcome the gap between the estimation and true distribution of class noise. Our proposed method does not require any a priori knowledge about class noises. We prove that the optimal classifier on the noisy distribution can approximate the optimal classifier on the clean distribution when the training set grows. Comparisons with existing algorithms show that our methods outperform state-of-the-art approaches on a large number of benchmark datasets in different domains. Both the theoretical analysis and the experimental result reveal that our method can improve the performance, works well on clean data and is robust on the algorithm parameter

Learning curves and long-term outcome of simulation-based thoracentesis training for medical students

<p>Abstract</p> <p>Background</p> <p>Simulation-based medical education has been widely used in medical skills training; however, the effectiveness and long-term outcome of simulation-based training in thoracentesis requires further investigation. The purpose of this study was to assess the learning curve of simulation-based thoracentesis training, study skills retention and transfer of knowledge to a clinical setting following simulation-based education intervention in thoracentesis procedures.</p> <p>Methods</p> <p>Fifty-two medical students were enrolled in this study. Each participant performed five supervised trials on the simulator. Participant's performance was assessed by performance score (PS), procedure time (PT), and participant's confidence (PC). Learning curves for each variable were generated. Long-term outcome of the training was measured by the retesting and clinical performance evaluation 6 months and 1 year, respectively, after initial training on the simulator.</p> <p>Results</p> <p>Significant improvements in PS, PT, and PC were noted among the first 3 to 4 test trials (p < 0.05). A plateau for PS, PT, and PC in the learning curves occurred in trial 4. Retesting 6 months after training yielded similar scores to trial 5 (p > 0.05). Clinical competency in thoracentesis was improved in participants who received simulation training relative to that of first year medical residents without such experience (p < 0.05).</p> <p>Conclusions</p> <p>This study demonstrates that simulation-based thoracentesis training can significantly improve an individual's performance. The saturation of learning from the simulator can be achieved after four practice sessions. Simulation-based training can assist in long-term retention of skills and can be partially transferred to clinical practice.</p

Emergence of Fatal PRRSV Variants: Unparalleled Outbreaks of Atypical PRRS in China and Molecular Dissection of the Unique Hallmark

Porcine reproductive and respiratory syndrome (PRRS) is a severe viral disease in pigs, causing great economic losses worldwide each year. The causative agent of the disease, PRRS virus (PRRSV), is a member of the family Arteriviridae. Here we report our investigation of the unparalleled large-scale outbreaks of an originally unknown, but so-called “high fever” disease in China in 2006 with the essence of PRRS, which spread to more than 10 provinces (autonomous cities or regions) and affected over 2,000,000 pigs with about 400,000 fatal cases. Different from the typical PRRS, numerous adult sows were also infected by the “high fever” disease. This atypical PRRS pandemic was initially identified as a hog cholera-like disease manifesting neurological symptoms (e.g., shivering), high fever (40–42°C), erythematous blanching rash, etc. Autopsies combined with immunological analyses clearly showed that multiple organs were infected by highly pathogenic PRRSVs with severe pathological changes observed. Whole-genome analysis of the isolated viruses revealed that these PRRSV isolates are grouped into Type II and are highly homologous to HB-1, a Chinese strain of PRRSV (96.5% nucleotide identity). More importantly, we observed a unique molecular hallmark in these viral isolates, namely a discontinuous deletion of 30 amino acids in nonstructural protein 2 (NSP2). Taken together, this is the first comprehensive report documenting the 2006 epidemic of atypical PRRS outbreak in China and identifying the 30 amino-acid deletion in NSP2, a novel determining factor for virulence which may be implicated in the high pathogenicity of PRRSV, and will stimulate further study by using the infectious cDNA clone technique

Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method

Based on the improved three-dimensional finite element model, this paper studies the load transfer mechanism of pile-supported reinforced embankments. The model uses an elastic medium to replace the soft soil subgrade, which reduces the calculation depth of the subgrade and improves the calculation efficiency of the model. The validity of the model is proven by field test results and theoretical calculation results. By changing the cohesion, internal friction angle, elastic modulus of the embankment filler, and geogrid strength, the effects of various influencing factors on the pile&ndash;soil stress ratio, the load-sharing ratio of the soil arching effect, and the load-sharing ratio of the membrane effect was analyzed, and the sensitivity of each influencing factor was evaluated. Based on the response surface optimization test, the multiple regression equation of influencing factors and evaluation indicators was established. The interaction between the parameters was analyzed, and the optimal combination of parameters was solved. The results show the following: Increasing the cohesion, the internal friction angle, and the elastic modulus of the embankment filler can promote the soil arching effect to a certain extent. However, for reinforced embankments, a large cohesion, a large internal friction angle, and a high elastic modulus of an embankment will reduce the pile&ndash;soil differential settlement and the pile&ndash;soil stress ratio; an increase in geogrid strength has a certain promoting effect on the pile&ndash;soil stress ratio. When the geogrid strength reaches 120 kN/m, the pile&ndash;soil stress ratio tends to be stable; the tested regression model can accurately reflect the changes in the relationship between the influencing factors and the response values, and it fits the actual situation well. Numerical simulation results show that the optimized pile&ndash;soil stress ratio increases by 13.4%

A Fuzzy-Rough Approach for Case Base Maintenance

. This paper proposes a fuzzy-rough method of maintaining CaseBased Reasoning (CBR) systems. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which take the form of fuzzy rules generated bythe rough set technique. In paper [1], we have proposed a methodology for case base maintenance which used a fuzzy decision tree induction to discover the adaptation rules; in this paper, we focus on using a heuristic algorithm, i.e., a fuzzy-rough algorithm [2] in the process of simplifying fuzzy rules. This heuristic, regarded as a new fuzzy learning algorithm, has many significant advantages, such as rapid speed of training and matching, generating a family of fuzzy rules which is approximately simplest. By applying such a fuzzy-rough learning algorithm to the adaptation mining phase, the complexity of case base maintenance is reduced, and the adaptation knowledge is more compact and effective. The effectiveness of the method is demonstrated experimentally using two sets of testing data , and we also compare the maintenance results of using fuzzy ID3, in [1], and the fuzzy-rough approach, as in this paper.