The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB) and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.

Background: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights. Results: In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches. Conclusions: Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

Fine-grained image recognition (FGIR), unlike traditional coarse-grained recognition, is centered on distinguishing fine-level subclasses within broader semantic categories. It holds significant scientific research value, particularly in remote sensing, where the precise identification of specific objects—such as ships, buildings, and land use categories— is critical for tasks like boundary security, environmental monitoring, and urban planning. Recent advancements in FGIR have notably improved feature representation and generalization, especially under the diverse imaging conditions typical of remote sensing. However, challenges remain, including the heavy reliance on high-quality, large-scale fine-grained image data and difficulties in extracting subtle image features. Efficiently utilizing limited data and enhancing feature extraction capabilities have thus become key focus areas in current FGIR research. This paper systematically reviews the advancements in FGIR, covering its foundational principles, key methodologies, and the latest research developments, while providing a comprehensive comparative analysis of their performance in remote sensing images applications. Additionally, it addresses the specific challenges posed by fine-grained recognition in remote sensing imagery and explores potential directions for future research in this field

Hyperspectral images (HSIs) include hundreds of spectral bands, which lead to Hughes phenomenon in classification task and decrease the classification accuracy. Feature selection can remove redundant and noisy features in the HSIs to overcome this phenomenon. In real applications, we may face a HSI scene with only a few labeled samples. Meanwhile, there are adequate labeled samples in a similar HSI scene. For example, they share the same land-cover classes. The shared information can be used to help the scene with a few labeled samples in feature selection. Traditional single-scene-based feature selection appears powerless in solving such problems. Cross-scene feature selection provides an attractive way to select feature subsets by simultaneously using the information from two HSI scenes. However, spectral distribution may change due to atmospheric conditions, yielding spectral shift. In order to tackle this problem, we propose a cross-domain algorithm based on hybrid whale optimization algorithm with simulated annealing (WOASA). The newly proposed algorithm is dubbed cross-domain WOASA (CDWOASA). CDWOASA simultaneously considers the separability of different land-cover classes and the consistency of selected features between two scenes, leading to discriminative and domain-invariant characters of selected feature subset. Moreover, since the original WOASA is not able to precisely control the dimension of selected features, we propose an improvement using a sorting strategy based on the fitness function value, thus making the output feature dimension precisely controlled. The experimental results on two cross-scene HSI datasets demonstrate the superiority of CDWOASA in cross-scene feature selection.

Lack of labeled training samples is a big challenge for hyperspectral image (HSI) classification. In recent years, cross-scene classification has become a new research topic. In cross-scene classification, two closely related HSI scenes are considered, one contains adequate labeled samples, namely source scene, while the other one contains only a few labeled samples, namely target scene. The goal of cross-scene classification is utilizing the labeled samples in source scene to benefit the classification in target scene. In most cases, different HSIs are imaged by different sensors, leading to different feature dimensions (numbers of bands) in different scenes. In this situation, heterogeneous transfer learning is demanded. In this article, we propose a heterogeneous transfer learning algorithm namely semisupervised dual-dictionary nonnegative matrix factorization (SS-DDNMF). SS-DDNMF consists of two contributions. 1) Dual-dictionary nonnegative matrix factorization (DDNMF): DDNMF trains two dictionaries for source and target scenes, respectively, aiming at projecting the source and target features to a shared low-dimensional subspace, eliminating the difference between feature spaces. In DDNMF, within-scene and cross-scene graphs are built to maintain the similarities between pixels. 2) Semisupervised learning for target scene: as the limited number of labeled pixels in target scene will affect the graph building of DDNMF, semisupervised learning is adopted in target scene. In details, superpixel segmentation is adopted to generate pseudolabels for some unlabeled pixels, thus more “labeled” pixels can be considered for building better graphs. The effectiveness of SS-DDNMF is verified by experiments on cross-scene HSIs.

In the classification of hyperspectral images (HSIs), too many spectral bands (features) cause feature redundancy, resulting in a reduction in classification accuracy. In order to solve this problem, it is a good method to use feature selection to search for a feature subset which is useful for classification. Iterative ReliefF (I-ReliefF) is a traditional single-scene-based algorithm, and it has good convergence, efficiency, and can handle feature selection problems well in most scenes. Most single-scene-based feature selection methods perform poorly in some scenes (domains) which lack labeled samples. As the number of HSIs increases, the cross-scene feature selection algorithms which utilize two scenes to deal with the high dimension and low sample size problem are more and more desired. The spectral shift is a common problem in cross-scene feature selection. It leads to difference in spectral feature distribution between source and target scenes even though these scenes are highly similar. To solve the above problems, we extend I-ReliefF to a cross-scene algorithm: cross-domain I-ReliefF (CDIRF). CDIRF includes a cross-scene rule to update feature weights, which considers the separability of different land-cover classes and the consistency of the spectral features between two scenes. So CDIRF can effectively utilize the information of source scene to improve the performance of feature selection in target scene. The experiments are conducted on three cross-scene datasets for verification, and the experimental results demonstrate the superiority and feasibility of the proposed algorithm.