MOTIVATION: Spatial transcriptomics (ST) enables the measurement of gene expression while preserving the spatial context of tissues. However, the sparsity of ST data leads to poor usage of gene expression and spatial information, resulting in the embeddings that are not well represented and challenging for downstream analyses. RESULTS: Here, we introduced GSG, a generative self-supervised representation learning framework for ST data that leverages a masking mechanism to learn informative representations. For spatial domain identification, GSG consistently outperformed state-of-the-art methods across benchmarking datasets, regardless of sequencing platforms. In addition, we applied GSG to an in-house human fetal heart dataset, revealing anatomically coherent spatial domains and identifying APCDD1 as an endocardial-specific marker potentially involved in congenital heart disease. Our results showcase GSG's superiority and underscore its valuable contributions to advancing ST analysis. AVAILABILITY AND IMPLEMENTATION: Our software package is available at https://github.com/keaml-Guan/GSG.

Clinical studies indicate that the progression of Type 2 Diabetes Mellitus (T2DM) is associated with characteristic alterations in tongue features, which may facilitate non-invasive early detection. However, current deep learning-based tongue imaging approaches for diabetes diagnosis remain constrained by limited datasets, subtle feature variations, dependence on clinical expertise, and the lack of quantitative evaluation. To address these issues, we developed an open-source dataset for T2DM tongue diagnosis (DMT) and benchmarked it using multiple baseline models. Building on DMT, we propose T2Net, a tongue image recognition model for T2DM that simulates the clinical diagnostic process. T2Net comprises four core components: local inspection, pathological clue integration, syndrome identification, and diagnostic confidence estimation. First, T2Net automatically extracts key ROIs by combining large-kernel decomposition with multi-scale learning. Then, a multi-order feature interaction module enables effective fusion of tongue image features across scales to capture pathological clues. Meanwhile, we design a context-aware dynamic aggregation convolution to model long-range dependencies, and propose a flexible focal loss to mimic the diagnostic reasoning process of clinicians, enabling brain-inspired inference. Finally, we propose a clustering-based confidence estimation approach to quantitatively evaluate the reliability of model predictions. Experimental results demonstrate that T2Net achieves highly competitive performance on the DMT dataset, outperforming the second-best baseline by 2.7% in accuracy and 2.0% in F1 score. Moreover, the quantitative evaluation scores are largely consistent with clinical assessments by physicians.

Clustering techniques offer strong interpretability. However, they have significant limitations in the deep learning area due to their difficulty in capturing complex data structures, such as spatial and contextual information. This issue is especially pronounced in neuroimaging research, where high-dimensional and complex data greatly restricts the feature representation capability of clustering models. Hence, we propose a Neuroimaging Deep Clustering (NIDC) backbone network. We convert 3D neuroimagings into point sets and design clustering-based paradigms for context feature aggregation, feature interaction, and feature dispatching to enable deep feature extraction from the point sets. To better capture spatial information, we propose brain spatial relative position encoding, which assists the clustering paradigm in better understanding the anatomical structure of brain tissue and the positional relationships between different regions. Additionally, we design a sample center loss function to encourage tighter clustering of labels or voxels/feature points of the same class in the feature space, aiming to suppress both inter-class and intra-class similarities. Meanwhile, NIDC preserves the interpretability of traditional clustering techniques, allowing it to uncover relationships between brain regions and trace the decision-making process of each voxel. NIDC achieves highly competitive performance across multiple datasets in various downstream tasks, emerging as a new and practical solution for neuroimaging analysis. Code is available athttps://github.com/IMCTGD/NIDC.

In the diagnosis of neurodegenerative diseases, effectively integrating heterogeneous data from medical imaging and clinical diagnostic texts remains a significant challenge. In this study, we propose CAFNet, a novel collaborative learning frame-work designed to bridge the gap between structural MRI and clinical narratives through anatomically guided and symptom-aware fusion. The framework leverages a large language model to encode rich semantic information from diagnostic text, while a anatomical MaxPooling Convolution module extracts region-specific features from brain MRI volumes using directional pooling along canonical axes. These heterogeneous features are further aligned via a symptoms attention convolution and refined through a Category-Aware Filtering module based on mutual information, which adaptively enhances class-relevant repre-sentations. Experimental evaluations on three public datasets demonstrate that CAFNet significantly improves diagnostic accuracy and generalization, outperforming existing state-of-the-art multimodal classification models. The results confirm the effectiveness of integrating anatomical priors and clinical semantics for interpretable and robust brain disease classification.

One-shot talking head synthesis aims to animate a source person’s portrait with driving video sequences. Recent facial keypoint-based methods have achieved remarkable animation performance and produced high-quality results. However, it remains challenging to perform cross-identity face reenactment by transferring subtle facial motions with correct geometry and appearance. To break the above limitations, in this paper, we propose a subtle motion compensation network to recover correct facial expressions by leveraging the decoupled 3D Morphable Model (3DMM) coefficient. In addition, to generate faithful animation results, a facial appearance feature memory bank is designed to learn accurate facial features and better recover the appearance. Experimental results have demonstrated that our proposed model can outperform state-of-the-art methods by generating faithful videos with correct subtle motion transfer and consistent identity preserving.

We propose a novel neural network architecture, the Lorentz Transformation Neural Network (LTNN), which utilizes Lorentz transformations to generate a complex computation matrix that enhances the network’s expressive power. Furthermore, LTNN is lightweight due to the shared weight matrices in the computation matrix. LTNN treats the input and output as coordinates in high-dimensional spacetime, with the weight matrices in each layer representing the velocity components of a spacetime reference frame. During training, these weight matrices are transformed into a computation matrix via Lorentz transformations, describing the coordinate transformations between different reference frames. We evaluate LTNN on four datasets: California Housing Prices, Iris, MNIST, and Fashion-MNIST. Experimental results demonstrate that LTNN outperforms conventional neural networks and quaternion neural networks in terms of both accuracy and parameter efficiency.

In recent years, deep reinforcement learning (DRL) has demonstrated significant potential to address complex and dynamic supply chain optimization (SCO) problems. However, existing DRL algorithms often encounter challenges when dealing with large-scale and high-dimensional supply chain decisions, making it difficult to effectively coordinate production and transportation. To address these issues, this paper proposes an innovative deep reinforcement learning algorithm—Action Decomposition Actor-Critic (ADAC). This algorithm significantly reduces learning complexity by decomposing complex decision tasks into multiple subtasks. Based on real-world supply chain scenarios, we construct a complex multi-stage supply chain environment. Additionally, we employ action decomposition-based Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms to learn optimal policies in continuous action spaces, enabling fine-grained control of production and transportation. To verify the effectiveness of the algorithm, we conduct extensive experiments on both simulation and real-world datasets. Experimental results demonstrate that the ADAC algorithm outperforms traditional heuristic algorithms and general DRL algorithms in multiple complex supply chain scenarios. This shows the strong robustness and wide applicability of the ADAC algorithm in SCO problems.

Alzheimer’s disease (AD) is the leading cause of dementia in the elderly and its incidence is rapidly increasing. sMRI data is important for early diagnosis of AD, but the scarcity of its labelled data makes supervised learning not good enough to train powerful deep learning models. Self-supervised learning (SSL) is a promising deep learning method that allows models to automatically learn their own features through a pretext task, and has achieved great success in natural image analysis. We propose AD-MAE for AD early classification with a self-supervised model based on a mask autoencoder (MAE) that employs a masking strategy tailored for sMRI. In order to capture both local details and global features, we incorporate a contrast learning (CL) branch, while employing a fusion loss function to address false positives and negatives in classification. Experimental results show that AD-MAE performs well in different task settings on the ADNI dataset and is expected to provide strong support for early diagnosis of AD.

The integration of multi-modal data and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer’s disease (AD). Despite the promising performance of existing deeplearning-based multi-modal neuroimaging models, these models often either fail to distinguish and utilize the unique strengths of FDG-PET in highlighting metabolic activity and the high-resolution depiction of anatomical details by sMRI, or neglect the complementary relationships between the two modalities. To address the above limitations, we propose an adaptive mask attention dual branch CNN-Transformer model. First, the dual-branch feature extractor that simultaneously captures the unique pathological changes in both sMRI and FDG-PET data while identifying common patterns between the two modalities. Next, adaptive masked attention facilitates interaction between different modalities and identifies common features between them. Additionally, a fusion module performs adaptive fusion of multi-modal features at both the channel and spatial levels. Ultimately, the Kolmogorov-Arnold Network (KAN) is utilized in place of the traditional Multi-Layer Perceptron (MLP) for AD decision-making. The experimental results on the ADNI database show that the proposed model achieves excellent performance in different tasks, surpassing existing multi-modal AD prediction models.

How to effectively optimize the operation for a complex supply chain environment has been high on the agenda. Although the existing deep reinforcement learning methods have achieved success in certain applications, they still face limitations in complex supply chain environments, including difficulties in data sharing and a lack of digital collaboration, especially in multi-agent systems. In order to meet this challenge, we propose a novel multi-agent reinforcement learning algorithm based on dynamic optimistic weights (DO-QMIX), aiming at solving the shortcomings of the traditional weighted QMIX algorithm (WQMIX) in the simplicity of the weighting function. WQMIX employs two weighting schemes to handle multi-agent issues. However, its fixed weighting function restricts algorithm performance and hinders adaptability to dynamic, complex supply chain challenges. Therefore, we propose a dynamic weighting mechanism, which can adjust the weighting function in real time based on the changes in the environment, thus improving the overall efficiency. We construct a complex multi-stage supply chain environment in the real-world supply chain scenario and conduct many experiments using both real-world and simulated datasets. The experimental results demonstrate that DO-QMIX is significantly superior to the traditional multi-agent reinforcement learning algorithm in complex supply chain scenarios, especially in dealing with dynamic changes and complex decisions.

Facial Expression Recognition (FER) is a critical method for evaluating the emotional states of patients with mental disorders, playing a significant role in treatment monitoring. However, due to privacy constraints, facial expression data from patients with mental disorders is severely limited. Additionally, the more complex inter-class and intra-class similarities compared to healthy individuals make accurate recognition of facial expressions challenging. Therefore, we propose a Voluntary Facial Expression Mimicry (VFEM) experiment, which collected facial expression data from schizophrenia, depression, and anxiety. This experiment establishes the first dataset designed for facial expression recognition tasks exclusively composed of patients with mental disorders. Simultaneously, based on VFEM, we propose a Vision Transformer FER model tailored for Complex mental disorder patients (CmdVIT). CmdVIT integrates crucial facial expression features through both explicit and implicit mechanisms, including explicit visual center positional encoding and implicit sparse attention center loss function. These two key components enhance positional information and minimize the facial feature space distance between conventional attention and critical attention, effectively suppressing inter-class and intra-class similarities. In various FER tasks for different mental disorders in VFEM, CmdVIT achieves more competitive performance compared to contemporary benchmark models. Our works are available at https://github.com/yjy-97/CmdVIT.

Pre-extracted aortic dissection (AD) centerline is very useful for quantitative diagnosis and treatment of AD disease. However, centerline extraction is challenging because (i) the lumen of AD is very narrow and irregular, yielding failure in feature extraction and interrupted topology; and (ii) the acute nature of AD requires a quick algorithm, however, AD scans usually contain thousands of slices, centerline extraction is very time-consuming. In this paper, a fast AD centerline extraction algorithm, which is based on a local conformal deep reinforced agent and dynamic tracking framework, is presented. The potential dependence of adjacent center points is utilized to form the novel 2.5D state and locally constrains the shape of the centerline, which improves overlap ratio and accuracy of the tracked path. Moreover, we dynamically modify the width and direction of the detection window to focus on vessel-relevant regions and improve the ability in tracking small vessels. On a public AD dataset that involves 100 CTA scans, the proposed method obtains average overlap of 97.23% and mean distance error of 1.28 voxels, which outperforms four state-of-the-art AD centerline extraction methods. The proposed algorithm is very fast with average processing time of 9.54s, indicating that this method is very suitable for clinical practice.

Although federated learning (FL) has achieved outstanding results in privacy-preserved distributed learning, the setting of model homogeneity among clients restricts its wide application in practice. This article investigates a more general case, namely, model-heterogeneous FL (M-hete FL), where client models are independently designed and can be structurally heterogeneous. M-hete FL faces new challenges in collaborative learning because the parameters of heterogeneous models could not be directly aggregated. In this article, we propose a novel allosteric feature collaboration (AlFeCo) method, which interchanges knowledge across clients and collaboratively updates heterogeneous models on the server. Specifically, an allosteric feature generator is developed to reveal task-relevant information from multiple client models. The revealed information is stored in the client-shared and client-specific codes. We exchange client-specific codes across clients to facilitate knowledge interchange and generate allosteric features that are dimensionally variable for model updates. To promote information communication between different clients, a dual-path (model-model and model-prediction) communication mechanism is designed to supervise the collaborative model updates using the allosteric features. Client models are fully communicated through the knowledge interchange between models and between models and predictions. We further provide theoretical evidence and convergence analysis to support the effectiveness of AlFeCo in M-hete FL. The experimental results show that the proposed AlFeCo method not only performs well on classical FL benchmarks but also is effective in model-heterogeneous federated antispoofing. Our codes are publicly available at https://github.com/ybaoyao/AlFeCo.

Adaptive interpretable ensemble model based on three-dimensional Convolutional Neural Network (3DCNN) and Genetic Algorithm (GA), i.e., 3DCNN+EL+GA, was proposed to differentiate the subjects with Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) and further identify the discriminative brain regions significantly contributing to the classifications in a data-driven way. Plus, the discriminative brain sub-regions at a voxel level were further located in these achieved brain regions, with a gradient-based attribution method designed for CNN. Besides disclosing the discriminative brain sub-regions, the testing results on the datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) indicated that 3DCNN+EL+GA outperformed other state-of-the-art deep learning algorithms and that the achieved discriminative brain regions (e.g., the rostral hippocampus, caudal hippocampus, and medial amygdala) were linked to emotion, memory, language, and other essential brain functions impaired early in the AD process. Future research is needed to examine the generalizability of the proposed method and ideas to discern discriminative brain regions for other brain disorders, such as severe depression, schizophrenia, autism, and cerebrovascular diseases, using neuroimaging.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

Time series data with missing values are ubiquitous in real applications due to various unforeseen faults during data generation, storage, and transmission. Time-Series Data Imputation (TSDI) is thus crucial to many temporal data analysis tasks. However, existing works usually consider only one of the following two issues: (1) intra-feature temporal dependency, and (2) inter-feature correlation, leading to the overlook of complex coupling information in imputation. To achieve more accurate TDSI, we design a novel imputation model called TABiG, which delicately preserves the short-term, long-term, and inter-feature dependencies by attention mechanisms in a delay error-reduced bi-directional architecture. That is, it leverages GRU to model short-term temporal dependencies and adopts self-attention mechanisms hierarchically to capture long-term temporal dependencies and inter-feature correlations. The multiple self-attention mechanisms are nested in a bi-directional structure to alleviate the problem of delay errors in RNN-like structures. To facilitate model training with higher generalization, a masking strategy that mimics various extreme real missing situations beyond the simple random ones has been adopted for generating self-supervised learning tasks. Comprehensive experiments demonstrate that TABiG significantly outperforms most state-of-the-art imputation counterparts. Complementary results and source code can be accessed at https://github.com/Zhang2112105189/TABiG

Alzheimer’s disease (AD) is a typical neurodegenerative disease involving multiple pathogenic factors. Early detection is the key to effective treatment of AD. However, most methods are developed based on data from a single modality, and ignore the relationships among subjects. In machine learning problems, hypergraph can be used to express the relationships between objects. In light of this, a framework for early diagnosis of Alzheimer’s disease based on multimodal hypergraph attention network is proposed in this paper. Specifically, we combine multimodal features to construct cross modal hypergraph, which represents the high-order structural relationships among subjects. Finally, a hypergraph attention network is used to fuse hypergraphs and perform the final classification. Our experimental results on the Alzheimer Disease Neuroimaging Initiative (ADNI) database show that our proposed method has better classification performance than the most advanced methods.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.

Anomalous left coronary artery from pulmonary artery (ALCAPA) is a serious cardiac anomaly, and surgical repair is the main treatment for ALCAPA patients in clinical practice. Recently, 3D printing has been widely adopted in the surgical planning of ALCAPA, which can give surgeons an intuitive structure of the heart especially the coronary arteries. However, before 3D printing is conducted, experienced radiologists need to manually segment the coronary arteries on computed tomography angiography (CTA) images, which is time-consuming, tedious and biased. On the other hand, automatic coronary artery segmentation with normal structures has been extensively studied in the community, but cannot be effectively applied to ALCAPA due to the significant variation of coronary artery structure in ALCAPA. In this paper, we propose ImageALCAPA, the first 3D CTA image dataset of ALCAPA. The proposed dataset contains 30 ALCAPA CTA images, which is of decent size compared with existing medical imaging datasets. We further propose a baseline method that performs multi-task 2D- 3D ensemble for automatic segmentation of ALCAPA. It is shown by experiment that our baseline method outperforms popular existing works on coronary artery segmentation. However, as the highest average Dice Similarity Coefficient of coronary arteries is merely 65%, there is still much room for improvement. To facilitate further research on this challenging problem, our dataset and codes are released to the public [1].

As many real data sets (e.g., social, financial, and medical data sets) are successively generated in evolution with the ever-changing environment, classification for data stream with concept drift attracts increasing attention in the fields of machine learning and data mining. However, to the best of our knowledge, existing works mainly consider the concept drift issue while ignoring another common characteristic of real data, i.e., existence of awkward heterogeneity caused by mixture of numerical and categorical attributes. It is worth noting that tackling both the concept drift and heterogeneity problems together is exponentially more challenging than dealing with only one of them. This paper, therefore, proposes an ensemble learning approach for the classification of numerical-and-categorical-attribute data (also called mixed data hereinafter) under concept drift. We first design a unified metric to appropriately address the heterogeneity of numerical and categorical attributes. Then a base classifier that can appropriately fuse the information provided by the heterogeneous attributes is formed accordingly. Furthermore, to make the classification adapt to the complex concept drifts demonstrated on the heterogeneous attributes, two types of base classifier ensembles are dynamically learned on the fly. Experimental results on various real mixed data sets with concept drifts demonstrate the efficacy of the proposed method.

Data sets composed of a mixture of categorical and numerical attributes (also called mixed data hereinafter) are common in real-world cluster analysis. However, insightful analysis of such data under an unsupervised scenario using clustering is extremely challenging because the information provided by the two different types of attributes is heterogeneous, being at different concept hierarchies. That is, the values of a categorical attribute represent a set of different concepts (e.g., professor, lawyer, and doctor of the attribute "occupation"), while the values of a numerical attribute describe the tendencies toward two different concepts (e.g., low and high of the attribute "income"). To appropriately use such heterogeneous information in clustering, this paper therefore proposes a novel attribute representation learning method called Het2Hom, which first converts the heterogeneous attributes into a homogeneous form, and then learns attribute representations and data partitions on such a homogeneous basis. Het2Hom features low time complexity and intuitive interpretability. Extensive experiments show that Het2Hom outperforms the state-of-the-art counterparts.

How to rank participants of a sports tournament is of fundamental importance. While PageRank has been extensively used for this task, the algorithm’s superiority over simpler ranking methods has never been clearly demonstrated. We address this knowledge gap by comparing the performance of multiple ranking methods on synthetic datasets where the true ranking is known and the methods’ performance can be thus quantified by standard information filtering metrics. Using sports results from 18 major leagues, we calibrate a state-of-art model, a variation of the classical Bradley–Terry model, for synthetic sports results. We identify the relevant range of parameters under which the model reproduces statistical patterns found in the analyzed empirical datasets. Our evaluation of ranking methods on the synthetic datasets shows that PageRank outperforms the benchmark ranking by the number of wins only early in a tournament when a small fraction of all games have been played yet. Increased randomness in the data due to home team advantage, for example, further reduces the range of PageRank’s superiority. We propose a new PageRank variant that combines forward and backward propagation on the directed network representing the input sports results. The new method outperforms PageRank in all evaluated settings and, when the fraction of games played is sufficiently small and the sport is not too random, it outperforms also the ranking by the number of wins. Beyond the presented comparison of ranking methods, our work paves the way for designing optimal ranking algorithms for sports results data.

Alternative splicing enables a gene translating into different isoforms and into the corresponding proteoforms, which actually accomplish various biological functions of a living body. Isoform-isoform interactions (IIIs) provide a higher resolution interactome to explore the cellular processes and disease mechanisms than the canonically studied protein-protein interactions (PPIs), which are often recorded at the coarse gene level. The knowledge of IIIs is critical to map pathways, understand protein complexity and functional diversity, but the known IIIs are very scanty. In this paper, we propose a deep learning based method called DeepIII to systematically predict genome-wide IIIs by integrating diverse data sources, including RNA-seq datasets of different human tissues, exon array data, domain-domain interactions (DDIs) of proteins, nucleotide sequences and amino acid sequences. Particularly, DeepIII fuses these data to learn the representation of isoform pairs with a four-layer deep neural networks, and then performs binary classification on the learnt representation to achieve the prediction of IIIs. Experimental results show that DeepIII achieves a superior prediction performance to the state-of-the-art solutions and the III network constructed by DeepIII gives more accurate isoform function prediction. Case studies further confirm that DeepIII can differentiate the individual interaction partners of different isoforms spliced from the same gene. The code and datasets of DeepIII are available at http://mlda.swu.edu.cn/codes.php?name=DeepIII.

Direction information has been intensively investigated for Finger-Knuckle-Print (FKP) recognition. However, most existing direction-based KFP recognition methods are handcrafted, which are heuristic and require too much prior knowledge to engineer them. In this paper, we propose a discriminative direction binary feature learning (DDBFL) method for FKP recognition. We first propose a direction convolution difference vector (DCDV) to better describe the direction information of FKP images. Then, we learn a feature projection to convert the DCDV into binary codes, which are compact for the intra-class samples and more separable for the inter-class samples. Finally, we concatenate the block-wise histograms of the DDBFL codes to form the final descriptor for FKP recognition. Experimental results on the baseline PolyU FKP database demonstrate the competitive performance of the proposed method.

Based on some similarities between the knowledge system composed of the rules with priority orders and the sequential learning ahead masking (SLAM) model, an approach to enhance the generalization capabilities of the former with the help of the later is advocated. Firstly, the mapping from a rule to weights is realized. Secondly, the SLAM model is initialized to contain the knowledge from the rules and the generalization capabilities of the model are improved through the adjustment of the weights. Thirdly, based on the model, the approach can realize the incremental learning to grasp the knowledge containing in the newly added instances. Finally, the experimentations testify the obtained model has stronger generalization capabilities.

The attribute reduction and rule generation (the attribute value reduction) are two main processes for knowledge acquisition. A self-optimizing approach based on a difference comparison table for knowledge acquisition aimed at the above processes was proposed. In the attribute reduction process, the conventional logic computation was transferred to a matrix computation along with some added thoughts on the evolution computation used to construct the self-adaptive optimizing algorithm. In addition, some sub-algorithms and proofs were presented in detail. In the rule generation process, the orderly attribute value reduction algorithm (OAVRA), which simplified the complexity of rule knowledge, was presented. The approach provided an effective and efficient method for knowledge acquisition that was supported by the experimentation.