Intelligent Data Analysis - Volume 22, issue 3

Authors: Saito, Kazumi | Ohara, Kouzou | Kimura, Masahiro | Motoda, Hiroshi

Article Type: Research Article

Abstract: At its heart the act of reviewing is very subjective, but in reality many factors would influence user’s decision. This can be called social influence bias. We pick two factors, “Who” and “When” and discuss which factor is more influential when a user posts his/her own rate in an online review system. We consider two kinds of users: real and virtual. In the former each user has its own metric, but in the latter the metric is assigned to the order of review posting actions (rating). We propose a weighted multinomial generative model that can learn the factor metric quite …efficiently from a vast amount of data already available in many online review systems. If the model can explain the data well enough, this implies that such a social bias does exist. We evaluate the proposed method and confirm its effectiveness by five review datasets, and empirically clarify that there is no universal solution, but the social bias does exist. In reality the influential factor depends on each dataset, the majority of users is normal (average), and there are two small groups of users, each with high metric value and low metric value. Show more

Keywords: Social media, influence, review score, machine learning

DOI: 10.3233/IDA-173373

Citation: Intelligent Data Analysis, vol. 22, no. 3, pp. 639-657, 2018

Authors: Feng, Qinrong | Wang, Fenfen

Article Type: Research Article

Abstract: Soft set theory can be used as a general mathematical tool for dealing with uncertainty. Fuzzy soft set is a combination of fuzzy set and soft set. There has some progress associated with practical applications of fuzzy soft set theory, especially the use of fuzzy soft sets in decision making. In this paper, soft t -discernibility matrix is firstly introduced in fuzzy soft sets by combination of soft discernibility matrix and level soft sets. Secondly, some properties associated with the soft t -discernibility matrix in fuzzy soft sets are researched. Lastly, an algorithm of decision making …based on the soft t -discernibility matrix for fuzzy soft set is proposed, and comparative analysis shows that this algorithm is effective. Show more

Keywords: Soft set, fuzzy soft set, level soft sets, soft t-discernibility matrix, decision making

DOI: 10.3233/IDA-163345

Citation: Intelligent Data Analysis, vol. 22, no. 3, pp. 659-674, 2018

Authors: Du, Yijun | Lu, Xiaobo | Zeng, Weili | Hu, Changhui

Article Type: Research Article

Abstract: In practical application, the performances of face recognition are always affected by variations of expression, illumination and so on. To address this problem, an interval type-2 fuzzy linear discriminant analysis (IT2FLDA) method is proposed. In this paper, we first propose the supervised interval type-2 fuzzy C-Means (IT2FCM) algorithm. Moreover, the supervised IT2FCM is incorporated into linear discriminant analysis (LDA). In this method, the membership degree matrix of training samples belonging to each class and means of each class are firstly calculated by the supervised IT2FCM algorithm. They are then applied to the definition of fuzzy within-class scatter matrix and fuzzy …between-class scatter matrix, respectively. In doing so, means of each class that are estimated by the supervised IT2FCM can converge to a more desirable location than ones obtained by class sample average and fuzzy k-nearest neighbor (FKNN) method. Furthermore, the IT2FLDA is able to minimize the effects of uncertainties, find the optimal projective directions and make the feature subspace discriminating and robust, which inherits the benefits of the supervised IT2FCM and LDA. The experiment results show that the IT2FLDA improves the recognition rate and reduces sensitivity to variations when compared to results from the previous techniques. Show more

Keywords: Type-2 fuzzy set, linear discriminant analysis, the supervised interval type-2 fuzzy C-Means, membership degree matrix

DOI: 10.3233/IDA-173365

Citation: Intelligent Data Analysis, vol. 22, no. 3, pp. 675-696, 2018