A generalized Gilbert algorithm and an improved MIES for one-class support vector machine

Indirect photodegradation of sulfadiazine in the presence of DOM: Effects of DOM components and main seawater constituents

The presence of pharmaceuticals and personal care products in coastal waters has caused concern over the past decade. Sulfadiazine (SD) is a very common antibiotic widely used as human and fishery medicine, and dissolved organic matter (DOM) plays a significant role in the indirect photodegradation of SD; however, the influence of DOM compositions on SD indirect photodegradation is poorly understood. The roles of reactive intermediates (RIs) in the indirect photolysis of SD were assessed in this study. The reactive triplet states of DOM (³DOM∗) played a major role, whereas HO· and ¹O₂ played insignificant roles. DOM was divided into four components using excitation-emission matrix spectroscopy combined with parallel factor analysis. The components included three allochthonous humic-like components and one autochthonous humic-like component. The allochthonous humic-like components contributed more to RIs generation and SD indirect photolysis than the autochthonous humic-like component. A significant relationship between the indirect photodegradation of SD and the decay of DOM fluorescent components was found (correlation coefficient, 0.99), and the different indirect photodegradation of SD in various DOM solutions might be ascribed to the different components of DOM. The indirect photolysis rate of SD first increased and then decreased with increasing pH. SD photolysis was enhanced by low salinity but remained stable at high salinity. The increased carbonate concentration inhibited SD photolysis, whereas nitrate showed almost no effect in this study.

MST-GEN: An Efficient Parameter Selection Method for One-Class Extreme Learning Machine

One-class classification (OCC) models a set of target data from one class to detect outliers. OCC approaches like one-class support vector machine (OCSVM) and support vector data description (SVDD) have wide practical applications. Recently, one-class extreme learning machine (OCELM), which inherits the fast learning speed of original ELM and achieves equivalent or higher data description performance than OCSVM and SVDD, is proposed as a promising alternative. However, OCELM faces the same thorny parameter selection problem as OCSVM and SVDD. It significantly affects the performance of OCELM and remains under-explored. This paper proposes minimal spanning tree (MST)-GEN, an automatic way to select proper parameters for OCELM. Specifically, we first build a n -round MST to model the structure and distribution of the given target set. With information from n -round MST, a controllable number of pseudo outliers are generated by edge pattern detection and a novel "repelling" process, which readily overcomes two fundamental problems in previous outlier generation methods: where and how many pseudo outliers should be generated. Unlike previous methods that only generate pseudo outliers, we further exploit n -round MST to generate pseudo target data, so as to avoid the time-consuming cross-validation process and accelerate the parameter selection. Extensive experiments on various datasets suggest that the proposed method can select parameters for OCELM in a highly efficient and accurate manner when compared with existing methods, which enables OCELM to achieve better OCC performance in OCC applications. Furthermore, our experiments show that MST-GEN can also be favorably applied to other prevalent OCC methods like OCSVM and SVDD.