Publications

Formed as generic building blocks being reflective of domain knowledge and experimental numeric evidence, information granules play a pivotal role in processing realized in Granular Computing and facilitating communication with the environment. In this study, we are concerned with a fundamental problem of constructing a collection of meaningful, easily interpretable spherical information granules with the use of the principle of justifiable granularity. The design process is formulated as an optimization problem. First, a series of numeric prototypes are determined around which information granules are constructed. Second, the values of radii of these information granules are optimized aiming at maximizing a certain performance index. Two alternatives of determining centers of information granules are compared, i.e., randomly selected numeric prototypes and prototypes generated with the aid of clustering. Two optimization criteria are also introduced and studied. Experimental studies involving synthetic data as well as data coming from the UCI Machine Learning repository are reported.

In order to capture/model uncertainty associated with imprecision or vagueness, a decision maker may express her/his judgments in terms of intuitionistic multiplicative preference relation (IMPR). Two important research topics with this regard are studied in the paper: 1) checking consistency of IMPR and 2) generating weights on the basis of this relation. A new definition of consistent IMPR is proposed. In light of this new definition, the properties of consistent IMPR are studied in detail. A transformation formula is proposed to construct a consistent IMPR from a given normalized intuitionistic fuzzy weight vector. By minimizing the differences between the constructed consistent IMPR and the given IMPR, some fractional programming models are developed to derive the intuitionistic fuzzy weight vector. Moreover, the models are also extended to group decision making. Finally, two numerical examples are provided to illustrate the effectiveness and practical relevance of the proposed models.

Fuzzy C-Means (FCM) is a widely used clustering method. However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process. In this stage, all samples are involved in the update of their non-affinity centers, and the fuzzy membership grades of the most of samples, whose assignment is unchanged, are still updated by calculating the samples-centers distances. All those lead to the algorithms converging slowly. In this paper, a new affinity filtering technique is developed to recognize a complete set of the non-affinity centers for each sample with low computations. Then, a new membership scaling technique is suggested to set the membership grades between each sample and its non-affinity centers to 0 and maintain the fuzzy membership grades for others. By integrating those two techniques, FCM based on new affinity filtering and membership scaling (AMFCM) is proposed to accelerate the whole convergence process of FCM. Many experimental results performed on synthetic and real-world data sets have shown the feasibility and efficiency of the proposed algorithm. Compared with the state-of-the-art algorithms, AMFCM is significantly faster and more effective. For example, AMFCM reduces the number of the iteration of FCM by 80% on average.

In the data-driven era, collecting high-quality labeled data requiring human labor is a common approach for training data-hungry models, called crowdsourcing. Recently, end-to-end learning from crowds has shown its flexibility and practicality. However, existing works in an end-to-end manner focus on learning after collecting labels, which results in noisy annotations and also requires cost. Inspired by computerized adaptive testing, we argue that the characteristics of workers should be mined as soon as possible to make the best use of talents. To this end, we propose an adaptive learning from crowds method, AdaCrowd, as a cost-effective solution. Specifically, we propose a probabilistic model to capture the informativeness of possible instances for each worker. The informativeness is considered to be the uncertainty of the annotation prediction model output in its current status. The adaptive learning procedure is optimized by maximizing data likelihood and can be used with existing crowdsourcing models. Extensive experiments are conducted on real-world datasets, LabelMe and CIFAR-10H. The experimental results, e.g., the reduction of annotations without performance degradation, demonstrate the effectiveness.