共查询到20条相似文献，搜索用时 437 毫秒
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《Information processing & management》2019,56(4):1300-1316
Dynamic Ensemble Selection (DES) strategy is one of the most common and effective techniques in machine learning to deal with classification problems. DES systems aim to construct an ensemble consisting of the most appropriate classifiers selected from the candidate classifier pool according to the competence level of the individual classifier. Since several classifiers are selected, their combination becomes crucial. However, most of current DES approaches focus on the combination of the selected classifiers while ignoring the local information surrounding the query sample needed to be classified. In order to boost the performance of DES-based classification systems, we in this paper propose a dynamic weighting framework for the classifier fusion during obtaining the final output of an DES system. In particular, the proposed method first employs a DES approach to obtain a group of classifiers for a query sample. Then, the hypothesis vector of the selected ensemble is obtained based on the analysis of consensus. Finally, a distance-based weighting scheme is developed to adjust the hypothesis vector depending on the closeness of the query sample to each class. The proposed method is tested on 30 real-world datasets with six well-known DES approaches based on both homogeneous and heterogeneous ensemble. The obtained results, supported by proper statistical tests, show that our method outperforms, both in terms of accuracy and kappa measures, the original DES framework. 相似文献
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《Information processing & management》2023,60(4):103378
To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. 相似文献
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A proposed particle swarm classifier has been integrated with the concept of intelligently controlling the search process of PSO to develop an efficient swarm intelligence based classifier, which is called intelligent particle swarm classifier (IPS-classifier). This classifier is described to find the decision hyperplanes to classify patterns of different classes in the feature space. An intelligent fuzzy controller is designed to improve the performance and efficiency of the proposed classifier by adapting three important parameters of PSO (inertia weight, cognitive parameter and social parameter). Three pattern recognition problems with different feature vector dimensions are used to demonstrate the effectiveness of the introduced classifier: Iris data classification, Wine data classification and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers. 相似文献
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Textual entailment is a task for which the application of supervised learning mechanisms has received considerable attention as driven by successive Recognizing Data Entailment data challenges. We developed a linguistic analysis framework in which a number of similarity/dissimilarity features are extracted for each entailment pair in a data set and various classifier methods are evaluated based on the instance data derived from the extracted features. The focus of the paper is to compare and contrast the performance of single and ensemble based learning algorithms for a number of data sets. We showed that there is some benefit to the use of ensemble approaches but, based on the extracted features, Naïve Bayes proved to be the strongest learning mechanism. Only one ensemble approach demonstrated a slight improvement over the technique of Naïve Bayes. 相似文献
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《Journal of The Franklin Institute》2023,360(12):8096-8117
Long-distance high-speed train localization based on distributed optical fiber sensors (DOFS) has been a challenging issue due to the large-scale heterogeneous sensor nodes. It requires a competent localization algorithm to be capable of strong generalization and quick response. This paper proposes a cooperative multi-classifier network (CMCN) for locating HSTs based on heterogeneous DOFS signals by adaptive modeling of the local characteristics. The proposed CMCN is composed of adaptive feature extraction, lightweight base classifiers and spatial boostrap aggregating (SBA). First, the heterogeneous signals are adaptively transformed to an optimal intrinsic mode function for extracting the statistical features of base classifiers. The base classifiers are constructed based on dynamic soft-margin support vector machine to model local characteristics without computationally burdensome kernel functions by introducing a dynamic penalty factor. The factor is automatically initialized by evaluating the regional consistency before training. Furthermore, the SBA estimates the location of HSTs based on the local states of nodes. It can cooperate with base classifiers for enhanced accuracy by searching for the interval with maximum regional consistency. Finally, a trial is conducted in a high-speed railway in China in long-term running of 92 days. The results prove feasibility and accuracy of the proposed algorithm. 相似文献
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以我国制造业上市公司为样本数据,用支持向量机作为基分类器的集成学习方法来预测企业的财务危机,通过具体实验分析可知:集成学习比单个基分类器的预测准确率提高了4个百分点,且稳定性更高,有效地提高了模型的预测精度,使得模型更具有准确性和应用性。基于支持向量机的集成学习方法在构建我国制造业上市公司财务危机预警模型上是有效的,且达到一定的财务危机预警效果。 相似文献
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为去除网络入侵数据集中的冗余和噪声特征,降低数据处理难度和提高检测性能,提出一种基于特征选择和支持向量机的入侵检测方法。该方法采用提出的特征选择算法选取最优特征组合,并以支持向量机为分类器建立模型,应用于入侵检测系统。仿真结果表明,本文方法不仅可以减少特征维数,降低训练和测试时间,还能提高入侵检测的分类准确率。 相似文献
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针对赤潮生物提出具有较高准确率的实时自动分类方法,本文提出采用ReliefF-SBS进行特征选择,即针对赤潮生物图像原始数据集进行特征分析,并在此基础上,对原始特征集进行特征选择以去除特征集中的无关特征和冗余特征,得到最优特征子集,减少它们对分类器分类精度的影响。文中给出了实验结果和分析,同时验证了对k-Nearest Neighbor algorithm(KNN)和Support Vector Machine(SVM)分类器分类效果的影响。 相似文献
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传统的数字识别算法存在识别速度、识别准确率和识别方法复杂度三者无法兼顾的问题,为解决该问题,提出了基于特征矩阵的高效数字识别算法。该算法首先在预处理的基础上获取字符的特征矩阵,然后用特征矩阵对字符的特征横线、竖线等特征进行提取,最后利用结构语句识别的方法实现数字识别。实验结果表明,基于特征矩阵的高效数字识别算法思路简单、速度快,且识别率达97％以上。 相似文献
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In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. However, associative classifiers also have some obstacles to overcome when they are applied in the area of text classification. The target text collection generally has a very high dimension, thus the training process might take a very long time. We propose a feature selection based on the mutual information between the word and class variables to reduce the space dimension of the associative classifiers. In addition, the training process of the associative classifier produces a huge amount of classification rules, which makes the prediction with a new document ineffective. We resolve this by introducing a new efficient method for storing and pruning classification rules. This method can also be used when predicting a test document. Experimental results using the 20-newsgroups dataset show many benefits of the associative classification in both training and predicting when applied to a real world problem. 相似文献
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架空输电线路铁塔结构是我国主要的输电方式,一旦发生损伤破坏将造成严重的经济损失。本文提出了一种基于随机森林的数据融合架空输电线路损伤识别方法。首先,采用多个传感器获取铁塔在不同损伤位置和程度上的振动加速度信号,并运用小波包对其进行多层分解;然后,将提取出来的各频带能量值构成特征向量输入到相应的随机森林进行训练和测试;最后,将多个随机森林分类器的次级决策进行数据融合,做出最终铁塔损失情况决策。应用该方法对500kV高压输电铁塔模型进行试验,并与单一分类器相比较。通过对实验数据的分析表明,该方法对铁塔损伤的识别效果优于单一RF分类器,可以有效地改善单一分类器的识别能力。同时也表明该方法具有较好的分类效果和容错能力。 相似文献
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《Information processing & management》2023,60(1):103088
Linear equations are valuable for real-world modeling phenomena involving at least one variable. However, verifying if the procedure followed by a human for solving a linear equation was done correctly is still a complicated matter. In this paper, we propose a methodology for the automatic character recognition and revision of the solving procedure of linear equations with one unknown. First, a camera is used to acquire an image of the handwritten solving procedure. Then, the image is pre-processed, and each character and equation lines are segmented. Subsequently, a convolutional neural network (CNN) is used to conduct the character recognition stage. Finally, a comparison rule is applied to revise the solving procedure. The character recognition was verified on a 2800 image data set (2100 for training and 700 for testing), including the ten digits and four symbols: ×, +, -, /. The revision procedure was tested on a data set with 140 handwritten equations (125 for training and 15 for testing). The results revealed that we recognized handwritten characters with an accuracy of 99%, which is similar to the state-of-the-art. Moreover, our proposal revised the solving procedure with an efficiency of 86.66%. 相似文献
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基于动态分类器集成的客户流失预测模型研究 总被引：1，自引：0，他引：1
目前大多数客户流失预测研究常采用单一预测模型.因此,本文将动态分类器组合与自组织数据挖掘理论(SODM)相结合,提出了基于SODM的动态分类器集成方法.以国内、国外电信公司客户流失预测数据为例,并与单一的预测模型以及已有的动态分类器组合方法进行了详细对比,发现该方法能在很大程度上提高客户流失预测的准确率、命中率以及提升系数,是进行客户流失预测的有效工具. 相似文献
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支持向量机是一种基于统计学习理论的机器学习方法,针对小样本情况表现出了优良的性能,目前被广泛应用于模式识别、函数回归、故障诊断等方面。这里主要研究支持向量机分类问题,着重讨论了以下几个方面的内容。首先介绍了支持向量机分类器算法,并将其应用于数据分类,取得了较高的准确率,所用数据来自于UCI数据集。仿真结果表明该算法具有较快的收敛速度和较高的计算精度。 相似文献
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分析了汽车牌照的几何特征和成像特点,提出基于自适应性阈值的搜索策略,对图像中的车牌进行定位;利用Hough变换对图像进行倾斜矫正;然后设计一个三级分类器,对单个字符进行模式匹配,得到识别结果,最终实现对原始车牌图像的识别。 相似文献
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基于CBERS-1图像的干旱半干旱区土地利用分类 总被引：5，自引：0，他引：5
以中巴资源卫星CBERS 1图像数据为信息源,分别采用最大似然法、BP神经网络和Fuzzy ARTMAP神经网络 3种分类器,以位于干旱区的中国新疆石河子地区为例,进行了土地利用计算机自动分类。结果认为,3种方法中以Fuzzy ARTMAP神经网络法分类精度最高,分别比最大似然法和BP神经网络法提高了 1 0.69%和 6.84%。同时也证实了CBERS 1图像在土地利用调查中的实用性 相似文献