Journal of Intelligent & Fuzzy Systems - Volume 41, issue 6

Authors: Ma, Zong-fang | Liu, Zhe | Luo, Chan | Song, Lin

Article Type: Research Article

Abstract: Classification of incomplete instance is a challenging problem due to the missing features generally cause uncertainty in the classification result. A new evidential classification method of incomplete instance based on adaptive imputation thanks to the framework of evidence theory. Specifically, the missing values of different incomplete instances in test set are adaptively estimated based on Shannon entropy and K -nearest centroid neighbors (KNCNs) technology. The single or multiple edited instances (with estimations) then are classified by the chosen classifier to get single or multiple classification results for the instances with different discounting (weighting) factors, and a new adaptive global fusion …method finally is proposed to unify the different discounted results. The proposed method can well capture the imprecision degree of classification by submitting the instances that are difficult to be classified into a specific class to associate the meta-class and effectively reduce the classification error rates. The effectiveness and robustness of the proposed method has been tested through four experiments with artificial and real datasets. Show more

Keywords: Incomplete instance, evidence theory, classification, missing data, uncertainty

DOI: 10.3233/JIFS-210991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7101-7115, 2021

Authors: Zeng, Shouzhen | Azam, Amina | Ullah, Kifayat | Ali, Zeeshan | Asif, Awais

Article Type: Research Article

Abstract: T-Spherical fuzzy set (TSFS) is an improved extension in fuzzy set (FS) theory that takes into account four angles of the human judgment under uncertainty about a phenomenon that is membership degree (MD), abstinence degree (AD), non-membership degree (NMD), and refusal degree (RD). The purpose of this manuscript is to introduce and investigate logarithmic aggregation operators (LAOs) in the layout of TSFSs after observing the shortcomings of the previously existing AOs. First, we introduce the notions of logarithmic operations for T-spherical fuzzy numbers (TSFNs) and investigate some of their characteristics. The study is extended to develop T-spherical fuzzy (TSF) logarithmic …AOs using the TSF logarithmic operations. The main theory includes the logarithmic TSF weighted averaging (LTSFWA) operator, and logarithmic TSF weighted geometric (LTSFWG) operator along with the conception of ordered weighted and hybrid AOs. An investigation about the validity of the logarithmic TSF AOs is established by using the induction method and examples are solved to examine the practicality of newly developed operators. Additionally, an algorithm for solving the problem of best production choice is developed using TSF information and logarithmic TSF AOs. An illustrative example is solved based on the proposed algorithm where the impact of the associated parameters is examined. We also did a comparative analysis to examine the advantages of the logarithmic TSF AOs. Show more

Keywords: T-Spherical fuzzy set, logarithmic operations, spherical fuzzy set, multi-attribute decision making methods

DOI: 10.3233/JIFS-211003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7117-7135, 2021

Authors: Wang, Xiaoyuan | Zhang, Lulu | Wang, Gang | Wang, Quanzheng | He, Guowen

Article Type: Research Article

Abstract: The collision risk of ships is a fuzzy concept, which is the measurement of the likelihood of a collision between ships. Most of existed studies on the risk of multi-ship collision are based on the assessment of two-ship collision risk, and collision risk between the target ship and each interfering ship is calculated respectively, to determine the key avoidance ship. This method is far from the actual situation and has some defects. In open waters, it is of certain reference value when there are fewer ships, but in busy waters, it cannot well represent the risk degree of the target …ship, since it lacks the assessment of the overall risk of the perceived area of the target ship. Based on analysis of complexity of ships group situation, the concept of relative domain was put forward and the model was constructed. On this basis, the relative collision risk was proposed, and the corresponding model was obtained, so as to realize risk assessment. Through the combination of real ship and simulation experiments, the variation trend, stability and sensitivity of the model were verified. The results showed that risk degree of the environment of ships in open and busy waters could be well assessed, and good references for decision-making process of ships collision avoidance could be provided. Show more

Keywords: Ships group situation, unmanned ship, relative domain, relative collision risk

DOI: 10.3233/JIFS-211025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7137-7150, 2021

Authors: Aaly Kologani, M. | Hoskova-Mayerova, S. | Borzooei, R. A. | Rezaei, G. R.

Article Type: Research Article

Abstract: In this paper, by using the concept of maximal filter of equality algebra, we introduce radical of equality algebra. Then some equivalence definitions of it and some related properties are investigated. Then by using this notion, we introduce the concept of semi-maximal filter and prime-like filter on equality algebras and the relation between them and other filters of equality algebra are investigated. Finally, by using the notion of prime-like filters, we introduce a topology on equality algebra.

Keywords: Equality algebra, maximal filter, radical, semi-maximal filter, prime-like filter

DOI: 10.3233/JIFS-211035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7151-7165, 2021

Authors: Zhou, Qing | Shi, Xi | Ge, Liang

Article Type: Research Article

Abstract: The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation …of question significance. Experiments demonstrate the effectiveness of our proposed methods. Show more

Keywords: Mental health, text analysis, interpretability, TF-IDF, Likert scale

DOI: 10.3233/JIFS-211044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7167-7179, 2021

Authors: Xu, Jie | Lv, Jian | Yang, Hong-Tai | Li, Yan-Lai

Article Type: Research Article

Abstract: The video conferencing software is regarded as a significant tool for social distancing and getting incorporations up and going. Due to the indeterminacy of epidemic evolution and the multiple criteria, this paper proposes a video conferencing software selection method based on hybrid multi-criteria decision making (HMCDM) under risk and cumulative prospect theory (CPT), in which the criteria values are expressed in various mathematical forms (e.g., real numbers, interval numbers, and linguistic terms) and can be changed with natural states of the epidemic. Initially, the detailed description of video conferencing software selection problem under an epidemic are given. Subsequently, a whole …procedure for video conferencing software selection is conducted, the approaches for processing and normalizing the multi-format evaluation values are presented. Furthermore, the expectations provided by DMs under different natural states of the epidemic are considered as the corresponding reference points (RP). Based on this, the matrix of gains and losses is constructed. Then, the prospect values of all criteria and the perceived probabilities of natural states are calculated according to the value function and the weighting function in CPT respectively. Finally, the proposed method is illustrated by an empirical case study, and the comparison analysis and the sensitivity analysis for the loss aversion parameter are conducted to prove the effectiveness and robustness. The results show that considering the psychological characteristics of DMs in selection decision is beneficial to avoid the unacceptable and potential loss risks. This study could provide a useful guideline for managers who intend to select appropriate video conferencing software. Show more

Keywords: Epidemic, video conferencing software selection, cumulative prospect theory, hybrid multi-criteria decision making under risk

DOI: 10.3233/JIFS-211054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7181-7198, 2021

Authors: Ma, Yanfang | Xu, Weifeng | Wang, Xiaoyu | Li, Zongmin | Lev, Benjamin

Article Type: Research Article

Abstract:  The decreasing resources of the earth and the deterioration of the environment are offering new challenges for handling waste management practices. The establishment of the smart waste bins plays an important role in promoting the development of waste classification and treatment fundamentally. We developed the evaluation system for the location selection problem of smart waste bins. Considering the uncertainty in the location selection of smart waste bins, the probabilistic linguistic term sets (PLTSs) are selected to express the evaluation information. Because of the excellent performance in weight-determing, the best worst method (BWM) is chosen to get the weight of criteria. …While the weighted aggregated sum product assessment (WASPAS) method could handle both the qualitative and quantitative information, which are considered to derive the final ranking of the alternatives. This paper proposed a new group multi-criteria decision making approach integrating the BWM and the WASPAS with probabilistic linguistic information. Finally, in the empirical example, a sensitivity analysis shows that the proposed method is stable, a comparison analysis with PL-TOPSIS, PL-VIKOR, and PL-TODIM reflects its effectiveness and rationality, and the managerial implication verifies its usefulness and practicability, which also give guide to the company, government and resident. Show more

Keywords: Multiple attributes decision making, BWM, WASPAS, probabilistic linguistic term set, smart waste bins

DOI: 10.3233/JIFS-211066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7199-7218, 2021

Authors: Cui, Xiaoning | Wang, Qicai | Zhang, Rongling | Dai, Jinpeng | Li, Sheng

Article Type: Research Article

Abstract: The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost …algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa. Show more

Keywords: Machine learning, prediction of Compressive strength, feature reorganization, XGBoost, data enhancement

DOI: 10.3233/JIFS-211088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7219-7228, 2021

Authors: Du, Quan | Feng, Kai | Xu, Chen | Xiao, Tong | Zhu, Jingbo

Article Type: Research Article

Abstract: Recently, many efforts have been devoted to speeding up neural machine translation models. Among them, the non-autoregressive translation (NAT) model is promising because it removes the sequential dependence on the previously generated tokens and parallelizes the generation process of the entire sequence. On the other hand, the autoregressive translation (AT) model in general achieves a higher translation accuracy than the NAT counterpart. Therefore, a natural idea is to fuse the AT and NAT models to seek a trade-off between inference speed and translation quality. This paper proposes an ARF-NAT model (NAT with auxiliary representation fusion) to introduce the merit of …a shallow AT model to an NAT model. Three functions are designed to fuse the auxiliary representation into the decoder of the NAT model. Experimental results show that ARF-NAT outperforms the NAT baseline by 5.26 BLEU scores on the WMT’14 German-English task with a significant speedup (7.58 times) over several strong AT baselines. Show more

Keywords: Neural machine translation, non-autoregressive translation, autoregressive translation, auxiliary representation fusion

DOI: 10.3233/JIFS-211105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7229-7239, 2021

Authors: Chu, Yongjie | Zhao, Lindu | Ahmad, Touqeer

Article Type: Research Article

Abstract: In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data …distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods. Show more

Keywords: Single sample per person, domain adaptation, discriminative feature learning, feature selection

DOI: 10.3233/JIFS-211106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7241-7255, 2021