Three-way decision with incomplete information based on similarity and satisfiability

Luo, Junfang; Hu, Mengjun; Qin, Keyun

doi:10.1016/j.ijar.2020.02.005

Cited by 43 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The whole process can be depicted as TAO (trisecting-acting-outcome) [25] framework shown in Figure 1. With the development of three-way decision, many applications of three-way decision were researched in various fields, such as, data analysis [26,27], concept analysis [28][29][30], granular computing [31,32], sequential three-way decision [33,34].…”

Section: Three-way Clusteringmentioning

confidence: 99%

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

2022

View full text Add to dashboard Cite

Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures.

show abstract

Section: Three-way Clusteringmentioning

confidence: 99%

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

2022

View full text Add to dashboard Cite

show abstract

“…i S i (10) The synaptic weight of the HNN-RAN3SAT can be pre-calculated [34] by comparing Equations (10) and (6). The important assumptions when considering P RAN3SAT into Equations ( 8)- (10) are:…”

Section: Ran3sat Representation In Hopfield Neural Networkmentioning

confidence: 99%

“…This is because the connection in C (3) will consist of connection with two variables as well. By substituting information in Equations ( 13) and ( 14) into Equation (10), the expanded representation of final energy for HNN-RAN3SAT is given as:…”

Section: Ran3sat Representation In Hopfield Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Novel Hopfield Neural Network Model with Election Algorithm for Random 3 Satisfiability

et al. 2021

View full text Add to dashboard Cite

One of the influential models in the artificial neural network (ANN) research field for addressing the issue of knowledge in the non-systematic logical rule is Random k Satisfiability. In this context, knowledge structure representation is also the potential application of Random k Satisfiability. Despite many attempts to represent logical rules in a non-systematic structure, previous studies have failed to consider higher-order logical rules. As the amount of information in the logical rule increases, the proposed network is unable to proceed to the retrieval phase, where the behavior of the Random Satisfiability can be observed. This study approaches these issues by proposing higher-order Random k Satisfiability for k ≤ 3 in the Hopfield Neural Network (HNN). In this regard, introducing the 3 Satisfiability logical rule to the existing network increases the synaptic weight dimensions in Lyapunov’s energy function and local field. In this study, we proposed an Election Algorithm (EA) to optimize the learning phase of HNN to compensate for the high computational complexity during the learning phase. This research extensively evaluates the proposed model using various performance metrics. The main findings of this research indicated the compatibility and performance of Random 3 Satisfiability logical representation during the learning and retrieval phase via EA with HNN in terms of error evaluations, energy analysis, similarity indices, and variability measures. The results also emphasized that the proposed Random 3 Satisfiability representation incorporates with EA in HNN is capable to optimize the learning and retrieval phase as compared to the conventional model, which deployed Exhaustive Search (ES).

show abstract

“…e most fundamental issue of the three-way decision is how to construct a trisection from a whole with reasonable interpretations. Researchers have constructed three regions based on rough sets [40][41][42][43], fuzzy sets [34], shadowed sets [44,45], intuitionistic fuzzy sets [46,47], vague sets [48] and soft sets [49]. A three-way decision model with an ordered relationship is defined as follows.…”

Section: Preliminariesmentioning

confidence: 99%

Action Strategy Analysis in Probabilistic Preference Movement-Based Three-Way Decision

Jiang

Zhao

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The trisecting-acting-outcome model is a methodology of “thinking in threes,” which is the main idea of the three-way decision (3WD). It consists of three components: trisecting, acting, and outcome evaluation. A strategy selection method in a movement-based three-way decision (M-3WD) has been proposed in previous work. However, conflicting information widely existing in the information system has not yet been given sufficient consideration. The conflicting information brings massive noisy strategies when mining action strategies in three regions. This paper proposed a novel three-way decision model for action strategy set, which can analyze and classify strategies by introducing credibility and coverage. The model can remove noisy strategies and choose strategies more suitable for the need of decision makers. To evaluate and select an optimal action strategy, we analyze the probabilistic preference in a movement-based three-way decision. The approach determines the probability of movement by using the evidence theory (D-S) theory. The optimal action strategy is selected by analyzing the difference between the ideal movement and the actual movement, the lower the difference, the better the strategy. We give an example of medical decision-making to illustrate the effectiveness of the proposed method.

show abstract

Three-way decision with incomplete information based on similarity and satisfiability

Cited by 43 publications

References 53 publications

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

Novel Hopfield Neural Network Model with Election Algorithm for Random 3 Satisfiability

Action Strategy Analysis in Probabilistic Preference Movement-Based Three-Way Decision

Contact Info

Product

Resources

About