Feature selection (FS) is an effective data preprocessing technique. In some practical applications, features may continuously arrive one by one or by groups, and we cannot know the exact number of features before learning. Streaming FS (SFS) aims to remove redundant and irrelevant features from the continuously arriving features. This article proposes a three-stage SFS method based on dynamic feature clustering and particle swarm optimization (SFS-DPSO). In the first stage, an online relevance analysis is utilized to quickly remove irrelevant features, reducing the size of newly arrived feature groups. In the second stage, a dynamic feature clustering technique is employed to divide redundant features into different groups, thereby reducing the search space for subsequent evolutionary algorithms. In the third stage, a historical information-driven integer particle swarm optimization algorithm is exploited to search for optimal feature subset in the clustered feature space. The proposed algorithm is applied in 12 typical datasets with different difficulty levels and a real-word case, experimental results show that it can achieve better-classification results in a reasonable time and is superior to most existing algorithms.

With the increase of the number of features and the sample size, existing feature selection (FS) methods based on evolutionary optimization still face challenges such as the “curse of dimensionality” and the high computational cost. In view of this, dividing or clustering the sample and feature spaces at the same time, this article proposes a hybrid FS algorithm using surrogate sample-assisted particle swarm optimization (SS-PSO). First, a nonrepetitive uniform sampling strategy is employed to divide the whole sample set into several small-size sample subsets. Regarding each sample subset as a surrogate unit, next, a collaborative feature clustering mechanism is proposed to divide the feature space, with the purpose of reducing both the computational cost of clustering feature and the search space of PSO. Following that, an ensemble surrogate-assisted integer PSO is proposed. To ensure the prediction accuracy of ensemble surrogate when evaluating particles, an ensemble surrogate construction and management strategy is designed. Since the whole sample set is replaced by a small number of surrogate units, SS-PSO significantly reduces the cost of evaluating particles in PSO. Finally, the proposed algorithm is applied to some typical datasets, and compared with six typical evolutionary FS algorithms, as well as its several variant algorithms. The experimental results show that SS-PSO can obtain good feature subsets at the smallest computational cost on most of datasets. All verify that SS-PSO is a highly competitive method for high-dimensional FS.

The "curse of dimensionality" and the high computational cost have still limited the application of the evolutionary algorithm in high-dimensional feature selection (FS) problems. This article proposes a new three-phase hybrid FS algorithm based on correlation-guided clustering and particle swarm optimization (PSO) (HFS-C-P) to tackle the above two problems at the same time. To this end, three kinds of FS methods are effectively integrated into the proposed algorithm based on their respective advantages. In the first and second phases, a filter FS method and a feature clustering-based method with low computational cost are designed to reduce the search space used by the third phase. After that, the third phase applies oneself to finding an optimal feature subset by using an evolutionary algorithm with the global searchability. Moreover, a symmetric uncertainty-based feature deletion method, a fast correlation-guided feature clustering strategy, and an improved integer PSO are developed to improve the performance of the three phases, respectively. Finally, the proposed algorithm is validated on 18 publicly available real-world datasets in comparison with nine FS algorithms. Experimental results show that the proposed algorithm can obtain a good feature subset with the lowest computational cost.

Band selection (BS) is a widely used dimensionality reduction technique for hyperspectral images. However, most of existing evolutionary algorithms focus on searching a globally optimal band subset under a fixed size, and their obtained band subsets may still contain a large number of redundant bands. In order to simultaneously obtain multiple optimal band subsets with different sizes, this article proposes an unsupervised multitask artificial bee colony (ABC) BS algorithm based on variable-size clustering (MBBS-VC). First, a variable-size band clustering method based on worst class decomposition is developed, based on which the BS problem can be modeled as a multitask optimization problem. Next, a multitask multimicrogroup bee colony algorithm with variable coding length is proposed to simultaneously search multiple optimal band subsets with different sizes. Moreover, several new strategies, including the intergroup collaboration strategy based on bidirectional neighborhood learning and the multimeasure integration judgment (MIJ) mechanism, are designed to improve the performance of MBBS-VC. In this article, the hyperspectral BS problem is transformed into a multitask optimization problem for the first time. Finally, compared with 15 classical BS algorithms on several commonly used datasets, experimental results verify the superiority of the proposed BS algorithm.

Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality” when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of “divide and conquer” in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.

Bowen ratio and energy balance (BREB) method is used to measure the evapotranspiration of mixed bush and grass in headwater re,lion of North China Plain. The results show that it is hard to observe the long-term data by BREB. To analyze the diurnal variation in the energy balance, three days (DOY 225, clear day. DOY 228, cloudy, fog in the morning, and 0.2mm of Precipitation, DOY 236, cloudy) in 2001 are selected from the growing period of vegetation. Actual evapotranspiration is 4.13, 0.95, and 3.38 mm/day, with mean value of 2.82 mm/day. Peak hourly ET is 0.66 mm/hour. Bowen ratio is 0.28, 0.30, and 0.35 on DOY 225, 228, and 236, respectively. The daily ET0 calculated by hourly Penmen-Monteith mothod on DOY 225, 228, and 236 is 4.68, 1.04, and 3.54 mm/day. Crop coefficient (Kc) is 0.88, 0.92, and 0.96, respectively. The variation of Kc is in agreement with the ET in clear day.

A change vector based method for extracting flooded area from multi-temporal ENVISAT ASAR image is presented in this paper. The high resolution and multi-polarisation modes of ENVISAT ASAR make itself a useful tool for flood monitoring. The vector change method, which has been used extensively to detect the change of land surface in optical remote sensing, can take advantage of the reference images information recorded during, the non-flooded period so it is more robust than other change detection approaches. This technology has been applied to monitor flood in Dongting Lake, China using six temporal ASAR data from June to October 2004.

Understanding of the characteristics of soil nutrients at the field and catchment scale is important for refining agricultural management practices and for improving sustainable land use. In order to analyze soil nutrient differences among different land use types and their relationships between land uses, 9 sampling sites including 3 land uses were selected in the Daqiaohe catchment on Yunnan of China. Significant differences in soil nutrients among these land uses were found. Higher values of soil nutrient in crop land soil, but lower values in forest soil and virgin soil. Nutrient contents decreased with soil profile depth increasing. All nutrient contents were poor in debris flow bottomland. At the same time, relationship between nutrient contents and grade contents was analyzed. The result showed that TP, AP and AK were significantly correlated with clay contents and slit contents at 0.01 level, while other nutrients were not correlated with clay and slit contents. There existed significant relationship between TP and sand contents, but for TN, the condition was found. However, nutrient was relation to many factors, thus relationship between nutrient contents and grade contents should he studied in future.