Many process variables are included in the wastewater treatment process. The realization of real-time detection of as many process variables as possible is of great significance for improve the quality of wastewater treatment. Due to cost constraints, it is difficult to achieve real-time monitoring of some important process variables. Soft measurement scheme, which modeling the relationship between easy-to-measure variables and hard-to-measure variables to achieve the best estimate of the latter is a common solution. This paper proposes a key variables soft measurement scheme for wastewater treatment process based on hierarchical extreme learning machine (HELM). The proposed scheme takes some of the known variables that are easy to measure as inputs, and then implement a best estimate of unknown variables that are difficult to measure. At the same time, the selective moving window strategy (SMW) is used to update the training datasets. Experiments show that the proposed scheme has excellent performance on many key indicators.
In recent years, event-based social network (EBSN) platforms have increasingly entered people’s daily life and become more and more popular. In EBSNs, event recommendation is a typical problem which recommends interested events to users. Different from traditional social networks, both online and off-line factors play an important role in EBSNs. However, the existing methods do not make full use of the online and off-line information, which may lead to a low accuracy, and they are also not efficient enough. In this paper, we propose a novel event recommendation model to solve the above shortcomings. At first, a feature extraction phase is constructed to make full use of the EBSN information, including spatial feature, temporal feature, semantic feature, social feature and historical feature. And then, we transform the recommendation problem to a classification problem and ELM is extended as the classifier in the model. Extensive experiments are conducted on real EBSN datasets. The experimental results demonstrate that our approach is efficient and has a better performance than the existing methods.
Bidirectional extreme learning machine (B-ELM) divides the learning process into two parts: At odd learning step, the parameters of the new hidden node are generated randomly, while at even learning step, the parameters of the new hidden node are obtained analytically from the parameters of the former node. However, some of the odd-hidden nodes play a minor role, which will have a negative impact on the even-hidden nodes, and result in a sharp rise in the network complexity. To avoid this issue, we propose a random orthogonal projection based enhanced bidirectional extreme learning machine algorithm (OEB-ELM). In OEB-ELM, several orthogonal candidate nodes are generated randomly at each odd learning step, only the node with the largest residual error reduction will be added to the existing network. Experiments on six real datasets have shown that the OEB-ELM has better generalization performance and stability than B-ELM, EB-ELM, and EI-ELM algorithms.
The big data trend has inspired feature-driven learning tasks, which cannot be handled by conventional machine learning models. Unstructured data produces very large binary matrices with millions of columns when converted to vector form. However, such data is often sparse, and hence can be manageable through the use of sparse random projections. This work studies efficient non-iterative and iterative methods suitable for such data, evaluating the results on two representative machine learning tasks with millions of samples and features. An efficient Jaccard kernel is introduced as an alternative to the sparse random projection. Findings indicate that non-iterative methods can find larger, more accurate models than iterative methods in different application scenarios.
With the increasing popularity of location-based social networks (LBSNs), an increasing number of people are sharing their locations with friends through check-in activities. Point-of-interest (POI) recommendation, in which new places are suggested to users, is one of the most important tasks in LBSNs. POI group recommendation aims to suggest the most agreeable places for a group of users. However, the most existing studies, based on collaborative filtering, suffered from various issues, such as: (1) data sparseness, (2) cold start, and (3) scalability. Moreover, many existing schemes are limited in functionality. To address the aforementioned issues, we proposes a POI group recommendation model from a new perspective. The model combines machine learning knowledge and considers POI group recommendation as a classification problem. Extreme learning machine (ELM) is used to train the features. ELM has fast learning speed and ensures the recommendation efficiency. Finally, a series of experiments verify the performance of the model with ELM.
