Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

A multiple power quality(MPQ)disturbance has two or more power quality(PQ)disturbances superimposed on a voltage signal.A compact and robust technique is required to identify and classify the MPQ disturbances.This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality(MPQ)disturbance using stockwell transform(ST)and hilbert transform(HT).This will reduce the computational time to identify theMPQdisturbances,whichmakes the algorithm fast.A MPQ identification index(IPI)is computed using statistical features extracted from the voltage signal using the ST and HT.IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances.A MPQ time location index(IPL)is computed using the features extracted from the voltage signal using ST and HT.IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time.Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio(SNR).The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree(RBDT)is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances.MATLAB software is used to perform the study.

Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.

The Decision Rules of Rough Set and its Application in Aquaculture

In this article,the basic theory of rough set is presented,followed by a new heuristics approach for rule reduction,and the procedure of rule mining in aquaculture is illuminated with an example.

Approach to stochastic multi-attribute decision problems using rough sets theory

Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.

Scheme of Cooperative Spectrum Sensing Based on Adaptive Decision Fusion Algorithm

Spectrum sensing is one of the core technologies for cognitive radios （CR）, where reliable detection of the signals of primary users （PUs） is precondition for implementing the CR systems. A cooperative spectrum sensing scheme based on an adaptive decision fusion algorithm for spectrum sensing in CR is proposed in this paper. This scheme can estimate the PU prior probability and the miss detection and false alarm probabilities of various secondary users （SU）, and make the local decision with the Chair-Varshney rule so that the decisions fusion can be done for the global decision. Simulation results show that the false alarm and miss detection probabilities resulted from the proposed algorithm are significantly lower than those of the single SU, and the performance of the scheme outperforms that of the cooperative detection by using the conventional decision fusion algorithms.

Clinical diagnosis of severe COVID-19:A derivation and validation of a prediction rule

BACKGROUND The widespread coronavirus disease 2019(COVID-19)has led to high morbidity and mortality.Therefore,early risk identification of critically ill patients remains crucial.AIM To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit(ICU)care.METHODS This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19,2020,and March 14,2020 in Shenzhen Third People’s Hospital.Multivariate logistic regression was applied to develop the predictive model.The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020,by area under the receiver operating curve(AUROC),goodness-of-fit and the performance matrix including the sensitivity,specificity,and precision.A nomogram was also used to visualize the model.RESULTS Among the patients in the derivation and validation datasets,38 and 9 participants(10.5%and 2.54%,respectively)developed severe COVID-19,respectively.In univariate analysis,21 parameters such as age,sex(male),smoker,body mass index(BMI),time from onset to admission(>5 d),asthenia,dry cough,expectoration,shortness of breath,asthenia,and Rox index<18(pulse oxygen saturation,SpO2)/(FiO2×respiratory rate,RR)showed positive correlations with severe COVID-19.In multivariate logistic regression analysis,only six parameters including BMI[odds ratio(OR)3.939;95%confidence interval(CI):1.409-11.015;P=0.009],time from onset to admission(≥5 d)(OR 7.107;95%CI:1.449-34.849;P=0.016),fever(OR 6.794;95%CI:1.401-32.951;P=0.017),Charlson index(OR 2.917;95%CI:1.279-6.654;P=0.011),PaO2/FiO2 ratio(OR 17.570;95%CI:1.117-276.383;P=0.041),and neutrophil/lymphocyte ratio(OR 3.574;95%CI:1.048-12.191;P=0.042)were found to be independent predictors of COVID-19.These factors were found to be significant risk factors for severe patients confirmed with COVID-19.The AUROC was 0.941(95%CI:0.901-0.981)and 0.936(95%CI:0.886-0.987)in both datasets.The calibration properties were good.CONCLUSION The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU.It assisted the ICU clinicians in making timely decisions for the target population.

A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation

Objective:To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease(CAD).Methods and Results:A total of 1,025 patients referred for coronary angiography were included in a retrospective,single-center study.Randomly,720 patients(70%)were selected as the development group and the other patients were selected as the validation group.Multivariate logistic regression analysis showed that the seven risk factors age,sex,systolic blood pressure,lipoprotein-associated phospholipase A 2,type of angina,hypertension,and diabetes were signifi cant for diagnosis of CAD,from which we established model A.We established model B with the risk factors age,sex,height,systolic blood pressure,low-density lipoprotein cholesterol,lipoprotein-associated phospholipase A 2,type of angina,hypertension,and diabetes via the Akaike information criterion.The risk factors from the original Framingham Risk Score were used for model C.From comparison of the areas under the receiver operating characteristic curve,net reclassifi cation improvement,and integrated discrimination improvement of models A,B,and C,we chose model B to develop the nomogram because of its fi tness in discrimination,calibration,and clinical effi ciency.The nomogram for diagnosis of CAD could be used easily and conveniently.Conclusion:An individualized clinical prediction model for patients with CAD allowed an accurate estimation in Chinese populations.The Akaike information criterion is a better method in screening risk factors.The net reclassifi-cation improvement and integrated discrimination improvement are better than the area under the receiver operating characteristic curve in discrimination.Decision curve analysis can be used to evaluate the effi ciency of clinical prediction models.

Dominance-based rough set approach as a paradigm of knowledge discovery and granular computing

Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.

Location Verification Systems in Emerging Wireless Networks

As location-based techniques and applications have become ubiquitous in emerging wireless networks, the verification of location information has become more important. In recent years, there has been an explosion of activity related to lo- cation-verification techniques in wireless networks. In particular, there has been a specific focus on intelligent transport systems because of the mission-critical nature of vehicle location verification. In this paper, we review recent research on wireless location verification related to vehicular networks. We focus on location verification systems that rely on for- mal mathematical classification frameworks and show how many systems are either partially or fully encompassed by such frameworks.

Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

An Effective Network Traffic Data Control Using Improved Apriori Rule Mining

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

Performance Analysis of Distributed Neyman-Pearson Detection Systems

The performance of a distributed Neyman-Pearson detection system is considered with the decision rules of the sensors given and the decisions from different sensors being mutually independent conditioned on both hypothese. To achieve the better performance at the fusion center for a general detection system of n 〉 3 sensor configuration, the necessary and sufficient conditions are derived by comparing the probability of detec- tion at the fusion center with that of each of the sensors, with the constraint that the probability of false alarm at the fusion center is equal to that of the sensor. The conditions are related with the performances of the sensors and using the results we can predict the performance at the fusion center of a distributed detection system and can choose appropriate sensors to construct efficient distributed detection systems.

Lane-changing decision rule with the difference of traffic flow's variation in multi-lane highway for connected and autonomous vehicles

Drivers are not far-sighted when they execute lane-changing manipulation.To address this issue,this study proposes a rule to improve vehicles'lane-changing decisions with accurate information of surrounding vehicles(e.g.time headway)-More specifically,connected and autonomous vehicles(CAVs)change lanes in advance if they find severer flow reducing in the lanes,while CAVs should maintain the car-following state if the variations of traffc flow in all lanes have a similar trend.To ilustrate the idea,this study frst calibrates two classic car-following models and a lane-changing model,and then conducts numerical simulations to illustrate the short-sighted decision of drivers.The study incorporates the idea into a lane-changing decision rule by changing the lane-changing model's pa-rameter,and conducts numerical tests to evaluate the effectiveness of the lane-changing decision rule in a multi-lane highway with a bottleneck.The results of this study indicate that the new lane-changing decision rule can substantially improve the throughput of the traffic flow,especially when the inflow exceeds the remaining capacity of the road.The lane-changing rule and results can bring insights into the control of CAVs,as well as the driver assistance system in connected vehicles.

Decision Making and Ability： An Explanation of Elitism in China＇s Government

This article tries to explain elitism in China＇s governmental decision making. Our model shows that the governments＇ expected utility increases with a bureaucrat＇s ability to make decisions under the flexible framework of delegation and communication （with separated reporting strategy）. In the early of 1950s, China＇s government choose a flexible decision making framework in order to efficiently manage many affairs in a complex environment. This initial choice started the process of a self-reinforcing demand for ability inside of the flexible decision making framework. With the current reforms of streamlining administrations and retreating from the market, the elitism of China＇s government might reverse.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.