A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

iRGD-reinforced,photo-transformable nanoclusters toward cooperative enhancement of intratumoral penetration and antitumor efficacy

Nano Research - Insufficient intratumoral penetration greatly hurdles the anticancer performance of nanomedicine. To realize highly efficient tumor penetration in a precisely and spatiotemporally...     

A Preamble Re-utilizing Access Scheme for Machine-Type Communications with Optimal Access Class Barring

Wireless Personal Communications - The existing long term evolution networks originally designed for human-to-human communications are hard to tackle numerous and bursty random access requests from...     

水泥窑余热发电系统用耐磨材料的研制与应用

本研究针对水泥窑余热发电系统的工作条件,根据耐磨材料的损毁机理,采用高铝矾土、煅烧氧化铝粉、黏土、硅微粉等为原料,以磷酸二氢铝为结合剂,以铝酸钙水泥为促硬剂,通过添加掩蔽剂和优化配料工艺,制备了水泥窑余热发电系统用耐磨材料,并对不同处理温度对耐磨材料的性能影响进行了分析。结果表明增强骨料与基质的结合能力有利于耐磨性的提高,磷酸及磷酸盐与氧化铝之间会随着温度的升高生成不同的磷酸铝相,都能够起到结合作用,比采用水泥结合更有利于提高材料在不同温度下的强度和耐磨性。使用结果表明,制备的耐磨材料施工性能优良,凝结硬化时间适当,强度高,抗冲刷性能好,在水泥窑余热发电系统中取得了良好的使用效果。