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Search Results (7)
  • Open Access


    ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

    Khudran M. Alzhrani*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5459-5479, 2022, DOI:10.32604/cmc.2022.028512

    Abstract Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure. Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task-specific outputs. The output layers’ weights are essential in transforming aggregated neurons outputs into tasks labels. This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks. The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance. The ensemble of a multi-output neural network that… More >

  • Open Access


    Soft Computing Based Evolutionary Multi-Label Classification

    Rubina Aslam1,*, Manzoor Illahi Tamimy1, Waqar Aslam2

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086

    Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking, Random k-label sets, and Hierarchy… More >

  • Open Access


    Study on Multi-Label Classification of Medical Dispute Documents

    Baili Zhang1, 2, 3, *, Shan Zhou1, Le Yang1, Jianhua Lv1, 2, Mingjun Zhong4

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1975-1986, 2020, DOI:10.32604/cmc.2020.010914

    Abstract The Internet of Medical Things (IoMT) will come to be of great importance in the mediation of medical disputes, as it is emerging as the core of intelligent medical treatment. First, IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution. Second, IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff. This information includes recommendation of similar historical cases, guidance for medical treatment, alerting of hired dispute profiteers etc. The multi-label classification of medical dispute documents (MDDs) plays an important role as a front-end process… More >

  • Open Access


    A Multi-Label Classification Method for Vehicle Video

    Yanqiu Cao1, Chao Tan1, Genlin Ji1, *

    Journal on Big Data, Vol.2, No.1, pp. 19-31, 2020, DOI:10.32604/jbd.2020.01003

    Abstract In the last few years, smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents, which causes many scholars to pay attention to autonomous driving. For this complexity scene, one of the major challenges is mining information comprehensively from massive features in vehicle video. This paper proposes a multi-label classification method MCM-VV (Multi-label Classification Method for Vehicle Video) for vehicle video to judge the label of road condition for unmanned system. Method MCM-VV includes a process of feature extraction and a process of multi-label classification. During feature extraction, grayscale, lane line and the edge of… More >

  • Open Access


    Multi-Label Chinese Comments Categorization: Comparison of Multi-Label Learning Algorithms

    Jiahui He1, Chaozhi Wang1, Hongyu Wu1, Leiming Yan1,*, Christian Lu2

    Journal of New Media, Vol.1, No.2, pp. 51-61, 2019, DOI:10.32604/jnm.2019.06238

    Abstract Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments about some hotels as a… More >

  • Open Access


    An Empirical Comparison on Multi-Target Regression Learning

    Xuefeng Xi1, Victor S. Sheng1,2,*, Binqi Sun2, Lei Wang1, Fuyuan Hu1

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI: 10.3970/cmc.2018.03694

    Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target learning. Among them, MTS performs… More >

  • Open Access


    Feature Selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis

    Shengqun Fang1, Zhiping Cai1,*, Wencheng Sun1, Anfeng Liu2, Fang Liu3, Zhiyao Liang4, Guoyan Wang5

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 419-433, 2018, DOI: 10.3970/cmc.2018.02289

    Abstract By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories, and compare this method… More >

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