Multi-attribute Classification Research Articles

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model’s potential for practical consumer metadata extraction.

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

As developing economies become more industrialized, the energy problem has become a major challenge in the twenty-first century. Countries around the world have been developing renewable energy to meet the Sustainable Development Goals (SDGs) of the United Nations (UN) and the 26th UN Climate Change Conference of the Parties (COP26). Leaders of enterprises have been made aware of the need to protect the environment and have been practicing environmental marketing strategies and green information systems (GISs) as part of ESG practices. With the rapid growth of the available data from renewable electricity suppliers, the analyses of multi-attribute characteristics across different fields of studies use data mining to obtain viable rule induction and achieve adaptive management. Rough set theory is an appropriate method for multi-attribute classification and rule induction. Nevertheless, past studies for Big Data analytics have tended to focus on incremental algorithms for dynamic databases. This study entails rough set theory from the perspective of the decrement decay alternative rule-extraction algorithm (DAREA) to explore rule induction and present case evidence with managerial implications for the emerging renewable energy industry. This study innovates rough set research to handle data deletion in a Big Data system and promotes renewable energy with valued managerial implications.

A Data Classifier Based on Maximum Likelihood Evidential Reasoning Rule

In Dempster–Shafer evidence theory (DST), some classical evidence combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as to increase the accuracy of multiattribute classification decision making. However, most of them have not yet considered the interdependence among multiple pieces of evidence. The newly proposed maximum likelihood evidential reasoning (MAKER) rule measures such ubiquitous interdependence by introducing correlation factors into evidence combination. Hence, this paper designs a MAKER-based classifier to mine more correlation information for data classification. Finally, some numerical analysis (classification) experiments are carried out using five popular benchmark databases from the University of California, Irvine (UCI) to illustrate that the refined measure for evidence interdependence can aggregate the fused probability (belief degree) into real class label of a sample and further improve classification accuracy.

Fair classification by loss balancing via fairness-aware batch sampling

Existing classification models often output discriminatory results since they learn the target attribute without addressing data imbalance with respect to the protected attributes (e.g., gender). The models tend to focus on learning toward demographic groups containing the larger number of training samples, which consequently leads to training loss discrepancy between the groups. Our work focuses on addressing the occurrence of training loss discrepancy between the groups to improve the model’s fairness. To this end, we firstly define the target-protected group using the target and protected attribute labels and observe the group-wise training loss in terms of previous fairness approaches. From the observation, we figure out that balancing the total loss across all the groups allows to mitigate fairness issue significantly, and meanwhile, only considering the sample size of each group to obtain a balanced mini-batch is not enough for mitigating fairness. Motivated by the observations, we propose a fairness-aware batch sampling scheme that adaptively updates batch sampling probability (BSP) and constructs a fairness-aware mini-batch from the model’s point of view. Our key idea is to balance the training losses via training with fairness-aware mini-batch. Through extensive experiments on two facial attribute benchmark datasets and one tabular dataset, our simple and effective sampling strategy achieves superior improvement in terms of two standard fairness metrics. We validate our algorithm with various experimental settings (e.g, multi-attribute classification, binary classification with multiple protected attributes). Moreover, we introduce a new metric for measuring the trade-off between fairness and classification performance. On this metric, our algorithm also achieves the best trade-off performance.

A multi-attribute recognition method of vehicle's line-pressing in parking lot based on multi-task convolution neural network

In order to solve the problems of low recognition accuracy and long recognition time, a multi-attribute recognition method based on multi-task convolution neural network is proposed. The structure principle of multi-task convolution neural network is analysed, and multi-task is set in the bottom area of convolutional neural network. The Hough transform is used to extract the parking line in the parking lot, and the input layer of the multi-attribute label structure is established by multi-attribute classification convolution neural network. The loss function of vehicle line pressing attributes in different parking lots is obtained by combining the full connection layer and the connecting sub layer. The multi-attribute recognition of vehicle pressure line is realised by measuring and learning the line voltage attributes of vehicles. The experimental results show that the method can effectively identify the line pressing situation of vehicles in parking lot, and the recognition accuracy can reach 99%.

Thin sand identification in complex depositional environment by supervised artificial neural networks

The thin sands within the study interval developed in a complex fluvial-to-shallow marine environment. This variety results in extremely heterogeneously distributed thin sands. The objectives are to identify heterogeneous thin sands via machine learning and evaluate the impact of tuning thickness on the recognition. Multi-attribute classification using supervised ANNs is employed to predict the distribution of these thin sands within six subintervals. Before the prediction, the investigation of the six subinterval shows that the major part of each subinterval is greater than the tuning thickness and can be resolved by the available seismic data. An integrated workflow of multi-attribute classification based on supervised ANNs was established. The supervised classification cannot only add significant details and enhanced lateral resolution, but also allows the interpreters to avoid manual labelling. The thin sands of the six intervals predicted by the supervised ANNs are verified qualitatively and quantitatively. The predicted sand thickness is validated by a log-based thickness map and shows similar distribution of thin sands. The cross plots of the sand thickness between the seismic predictions and the log measurement at well locations quantify the seismic prediction. Therefore, both verifications show seismic prediction characterizes the thin sands in the study area and supervised ANNs have great potential in solving the challenge of thin bed identification. The positive correlation of the coefficients between the predicted and log-based thickness at the well locations and the average thickness of the subintervals suggest that seismic prediction still depends on the tuning thickness though multi-attribute classification based on the supervised ANNs used.

A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours.

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.

Seismic Geometric Nonparallelism Attributes

Pattern recognition based on computer-assisted seismic classification accelerates traditional seismic interpretation and can automatically extract seismic patterns of interest on large data volumes that would take considerable effort by a human interpreter. Seismic facies such as salt domes, mass transport complexes, and karst collapse features, can often be differentiated from surrounding conformal sediments by lateral and vertical changes in dip, energy, and continuity. To better exploit the difference between conformal and more chaotic seismic features, we introduce 3-D geometric nonparallelism attributes to statistically quantify lateral and vertical changes in both dip and amplitude gradients. When examining the attribute response of salt and karst features, coherent noise or small internal reflector blocks in an otherwise chaotic background give rise to a “salt and pepper” pattern on coherence, reflector parallelism, and texture attributes. Whereas human interpreters see the larger patterns and can put them in the proper geological context, simple attribute classification techniques cannot and see such embedded coherent anomalies as separate facies. To address this issue, we apply a 3-D structure-oriented adaptive Kuwahara filtering technique to precondition the seismic attribute volumes to smooth the internal attribute response while sharpening the facies edges prior to subsequent machine learning classification. We validate our workflow by applying a semisupervised generative topographic mapping machine learning-based multiattribute classification algorithm of five seismic attribute volumes to mapping karst collapse facies.

A General Approach to Multiattribute Classification Problems Based on Verbal Decision Analysis

In this paper, we propose a general approach based on the verbal decision analysis (VDA) to ordinal, nominal–ordinal, and nominal multiattribute classification problems that is implemented in the STEPCLASS method. It includes the following techniques: application domain structuring with the determination of a classification problem type, flexible strategy of dialogue with the expert for acquiring a complete and consistent set of his/her classification rules with the possibility of modifying the application domain structure, and decomposition of large-size classification problems with the subsequent aggregation of classification rules over the set of all attributes.

Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules.

Lung cancer has the highest mortality rate of all cancers, and early detection can improve survival rates. In the recent years, low-dose CT has been widely used to detect lung cancer. However, the diagnosis is limited by the subjective experience of doctors. Therefore, the main purpose of this study is to use convolutional neural network to realize the benign and malignant classification of pulmonary nodules in CT images. We collected 1004 cases of pulmonary nodules from LIDC-IDRI dataset, among which 554 cases were benign and 450 cases were malignant. According to the doctors' annotates on the center coordinates of the nodules, two 3D CT image patches of pulmonary nodules with different scales were extracted. In this study, our work focuses on two aspects. Firstly, we constructed a multi-stream multi-task network (MSMT), which combined multi-scale feature with multi-attribute classification for the first time, and applied it to the classification of benign and malignant pulmonary nodules. Secondly, we proposed a new loss function to balance the relationship between different attributes. The final experimental results showed that our model was effective compared with the same type of study. The area under ROC curve, accuracy, sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, respectively.

Systematic workflow for reservoir characterization in northwestern Colombia using multi-attribute classification

Systematic workflow for reservoir characterization in northwestern Colombia using multi-attribute classification

A Two-Stage Attribute-Constraint Network for Video-Based Person Re-Identification

Person re-identification has gradually become a popular research topic in many fields such as security, criminal investigation, and video analysis. This paper aims to learn a discriminative and robust spatial–temporal representation for video-based person re-identification by a two-stage attribute-constraint network (TSAC-Net). The knowledge of pedestrian attributes can help re-identification tasks because it contains high-level information and is robust to visual variations. In this paper, we manually annotate three video-based person re-identification datasets with four static appearance attributes and one dynamic appearance attribute. Each attribute is regarded as a constraint that is added to the deep network. In the first stage of the TSAC-Net, we solve the re-identification problem as a classification issue and adopt a multi-attribute classification loss to train the CNN model. In the second stage, two LSTM networks are trained under the constraint of identities and dynamic appearance attributes. Therefore, the two-stage network provides a spatial–temporal feature extractor for pedestrians in video sequences. In the testing phase, a spatial–temporal representation can be obtained by inputting a sequence of images to the proposed TSAC-Net. We demonstrate the performance improvement gained with the use of attributes on several challenging person re-identification datasets (PRID2011, iLIDS-VID, MARS, and VIPeR). Moreover, the extensive experiments show that our approach achieves state-of-the-art results on three video-based benchmark datasets.

Modeling and problem solving of building defects using point clouds and enhanced case-based reasoning

Abstract Many factors, including improper maintenance and material aging, may lead to the occurrence of defects during the operation of the various functions of buildings. Building defect information is normally stored in a discrete and unstructured way, and for this reason, building a case-based reasoning framework regarding building defects to enhance the level of building maintenance management has become an important field in the related research. At present, there is limited research available on the integration of geometric data models that are built by means of scanning and multi-attribute selection strategies. This study proposes an integrated information management framework for superficial defects in buildings, which is compatible with a point clouds model as a central data source. It features the attributes of defects used in multi-criteria decision analysis. A CBR (case-based reasoning) approach that considers case-based distance is used to enhance the performance of similarity calculations and case retrieval. A case-based distance model is utilized for the data processing stage and concentrates on a smaller case set that contains best alternatives. The potential benefit offered by this approach is that more efficient results can be obtained from classified cases during the retrieval phase process. A comparison of a CBR query with ungrouped sample data is performed to establish patterns to verify the effectiveness of the calculation method of determining case similarity, which is supported by the pre-processing of classified information about the building defects. The analytical results show that the proposed method performs well in solving the multi-attribute classification of building defects and avoiding ambiguous answers retrieved from unrelated subsets. This approach might be capable of investigating the practical problems involved in building maintenance in the AEC domains.

Robustness analysis for decision under uncertainty with rule-based preference model

We consider decision under uncertainty as a multi-attribute classification problem where a set of acts is described by outcomes gained with given probabilities. The Decision Maker (DM) provides desired classification for a small subset of reference acts. Such preference information is structured using Dominance-based Rough Set Approach (DRSA), and the resulting lower approximations of the quality class unions are used as an input for construction of an aggregate preference model. We induce all minimal-cover sets of rules being compatible with the non-ambiguous assignment examples, and satisfying some additional requirements that may be imposed by the DM. Applying such compatible instances of the preference model on a set of all acts, we draw conclusions about the certainty of recommendation assured by different minimal-cover sets of rules. In particular, we analyze the diversity of class assignments, assignment-based preference relations, and class cardinalities. Then, we solve an optimization problem to get a univocal (precise) classification for all acts, taking into account the robustness concern. This optimization problem admits incorporation of additional indirect and imprecise preferences in form of desired class cardinalities and assignment-based pairwise comparisons. Finally, we extend the proposed approach to group decision under uncertainty. We present a set of indicators and outcomes giving an insight into the spaces of consensus and disagreement between the DMs.

Inventory Classification and Management Strategy of Components and Parts in Assembly Workshops

Material inventory management plays an increasingly important role in modern operations management within manufacturing enterprises. And a multi-attribute classification model has been put up based on the application of the decide tree model and fuzzy artificial neural network. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.

A Multi-attribute Classification Method on Fresh Agricultural Products

By analyzing the current situation of inventory management of enterprises of fresh agricultural products in Handan city as an example and based on the nature of fresh agricultural products, this paper points out the defect and disadvantage of single classification standard of ABC classification method in inventory classification; and applies the improved classification method based on ABC classification method and multi-attribute strategic method. By selecting multiple standards as sorting criterion, such as storage facility, influence of stockout, seasonality and availability, the paper studies the classification method of inventory management in order to reduce the logistic cost of fresh agricultural products in logistic system.

DOMINANCE-BASED EXTENSION OF STEPCLASS FOR MULTIATTRIBUTE NOMINAL CLASSIFICATION

The problem of knowledge-based multiattribute classification with nonorderable classes is considered within the Verbal Decision Analysis (VDA) paradigm. Two VDA-based methods for such problem solving — NORClass and STEPCLASS — are outlined. The STEPCLASS method is initially designed for the problem with nonordered values of attributes. The main idea of NORClass is based on the assumption that a domain expert is able to order the values of any attribute according to their typicality for each class differently and independently of the values of other attributes. We propose to integrate into the STEPCLASS method the main ideas from NORClass. It is shown that such integration allows increasing the efficiency of the STEPCLASS method in cases, where the above assumption is true, and to overcome some drawbacks of NORClass.

A multi-attribute classification fusion system for insulated gate bipolar transistor diagnostics

Effective health diagnosis provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a multi-attribute classification fusion system which leverages the strengths provided by multiple membership classifiers to form a robust classification model for insulated gate bipolar transistor (IGBT) health diagnostics. The developed diagnostic system employs a k-fold cross-validation model for the evaluation of membership classifiers, and develops a multi-attribute classification fusion approach based on a weighted majority voting with dominance scheme. An experimental study of IGBT degradation was first carried out for the identification of failure precursor parameters, and classification techniques (e.g., supervised learning, unsupervised learning, and statistical inference) were then employed as the member algorithms for the development of a robust IGBT classification fusion system. In this study, the developed classification fusion model based on multiple member classification algorithms outperformed each stand-alone method for IGBT health diagnostics by providing better diagnostic accuracy and robustness. The developed multi-attribute classification fusion system provides an effective tool for the continuous monitoring of IGBT health conditions and enables the development of IGBT failure prognostics systems.

Inductive discovery of laws using monotonic rules

We are considering knowledge discovery from data describing a piece of real or abstract world. The patterns being induced put in evidence some laws hidden in the data. The most natural representation of patterns-laws is by “ if..., then...” decision rules relating some conditions with some decisions. The same representation of patterns is used in multi-attribute classification, thus the data searched for discovery of these patterns can be seen as classification data. We adopt the classification perspective to present an original methodology of inducing general laws from data and representing them by so-called monotonic decision rules. Monotonicity concerns relationships between values of condition and decision attributes, e.g. the greater the mass (condition attribute), the greater the gravity (decision attribute), which is a specific feature of decision rules discovered from data using the Dominance-based Rough Set Approach (DRSA). While in DRSA one has to suppose a priori the presence or absence of positive or negative monotonicity relationships which hold in the whole evaluation space, in this paper, we show that DRSA can be adapted to discover rules from any kind of input classification data, exhibiting monotonicity relationships which are unknown a priori and hold in some parts of the evaluation space only. This requires a proper non-invasive transformation of the classification data, permitting representation of both positive and negative monotonicity relationships that are to be discovered by the proposed methodology. Reported results of a computational experiment confirm that the proposed methodology leads to decision rules whose predictive ability is similar to the best classification predictors. It has, however, a unique advantage over all competitors because the monotonic decision rules can be read as laws characterizing the analyzed phenomena in terms of easily understandable “ if..., then...” decision rules, while other predictor models have no such straightforward interpretation.