Optimal Rule Research Articles

Collaborative annealing power [formula omitted]-means++ clustering

Clustering is the most fundamental technique for data processing. This paper presents a collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ and power k-means algorithms in a collaborative neurodynamic optimization framework. The proposed algorithm starts with k-means++ to select initial cluster centers, then leverages the power k-means to find multiple sets of centers as alternatives and a particle swarm optimization rule to reinitialize the centers in the subsequential iterations for improving clustering performance. Experimental results on twelve benchmark datasets are elaborated to demonstrate the superior performance of the proposed algorithm to seven mainstream clustering algorithms in terms of 21 internal and external indices.

Clustering of noised and heterogeneous multi-view data with graph learning and projection decomposition

Complex systems in society and nature cannot be effectively modeled and represented by a single perspective, resulting in the so-called multi-view data, which provide an absolutely excellent chance to explore the fundamental mechanisms of systems. In comparison with single-view clustering, multi-view clustering simultaneously considers the intrinsic property within the same view and the relations across various views. Current methods for multi-view are criticized for the undesirable result because they fail to resolve the heterogeneity, consistency, and diversity of various views. To address these issues, we propose Consistency and Diversity Preserving with Projection Decomposition for multi-view clustering (aka CDP2D), in which the shared and specific projection matrices are automatically learned. Specifically, CDP2D constructs a graph for each view, transforming the multi-view clustering issue into multi-layer network clustering. Then, CDP2D separates the projection matrix of self-representation into common and specific parts with low-rank constraint: the former part characterizes the consistency of various views, and the latter part characterizes the specificity of each view. To ensure the diversity of various views, CDP2D minimizes the similarity among the view-specific parts, which is formulated as trace optimization. All these procedures are integrated into an overall objective function, and the optimization rules are derived. The extensive experimental results on 10 datasets indicate that CDP2D observably outperforms 12 state-of-the-art algorithms in terms of various measurements, implying that it is promising for analyzing multi-view data with projection matrix decomposition.

Filter feature selection based Boolean Modelling for Genetic Network Inference

The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency.

Intelligent Evaluation of Public Sports Service Based on Intuitionistic Fuzzy Set Theory.

At this stage, in the research process of sports public service efficiency evaluation and analysis, due to the use of accurate data analysis methods, there are problems of low evaluation reliability and low analysis speed. Based on this, this paper studies the application of an information strategy based on the intuitionistic fuzzy data matching method in the evaluation and analysis of sports public service efficiency. The efficiency evaluation and analysis model of sports public service based on the intuitive fuzzy information algorithm is established. According to the characteristics of different types of sports public service, different types of data analysis methods are used to evaluate the efficiency, and the optimization of the efficiency analysis model of sports public service is realized according to the service demand and data type of different sports events. Finally, an experiment is designed to quantitatively evaluate the accuracy, stability, and reliability of the sports public service analysis model. The results show that the efficiency intelligent evaluation and analysis model based on the intuitive fuzzy information algorithm can effectively select the optimal sports public service evaluation countermeasures and rules according to the characteristics and data types of different sports events, realize the multi-dimensional accuracy classification of different types of sports events, and effectively improve the reliability of sports public service efficiency evaluation.

Optimal consumption and portfolio choices in the stochastic SIS model

Motivated by the stochastic SIS model, this paper aims to incorporate the stochastic transmission shock (e.g., COVID−19) into the standard portfolio theory, and explores the optimal rules with respect to infections. The impact of COVID−19 is decomposed into two dimensions such as infectivity (denoted by R0) and infection rate (measured by I). The results indicate that infectivity mainly affects consumption, whereas the investment rule is merely governed by infection rate. Moreover, we find that higher infections lead to less sensitivity and smoother consumption compared with normal times. However, the sensitivity and volatility of investment with respect to infections present a U-shape and hump-shape, respectively. Notably, we shed new lights on the effect of transmission uncertainty and risk-aversion with pandemic shock. The innovative attempt of this paper not only enriches the research of epidemiology in the field of economics, but also provides a paradigm for studying investor’s behavior under the normalization of epidemic situations.

Perceptual quality assessment for no-reference image via optimization-based meta-learning

Image quality assessment (IQA) is a critical issue in computer vision, which intends to simulate human visual system (HVS) with a view to evaluating the error degree of distorted images from reference images. The algorithms based on deep learning have been successfully introduced to quality evaluation of no-referenced images in the last years. Unfortunately, they are confronted with the problems of over-fitting and weak generalization ability caused by insufficient labeled data. The emergence of meta-learning has brought new ideas, which have been proved to address the issues regarding few-shot learning. However, the commonly used meta-learning metrics only learn the initialization of weights, which can’t guarantee the optimal gradient direction. The manual design mode leads to lower accuracy and speed. In our work, an improved meta-learning framework is applied to no-reference (NR) IQA to meet the above challenges. It can achieve maximum generalization performance through only a few update iterations. Specifically, we collected a great many NR-IQA tasks with different distortions to pre-train meta-model and optimizer to learn a general weight initialization and optimization rule. Then, the meta-model acquired meta-knowledge and learned unique learning rate for each task. Finally, it could be directly adapted to new NR-IQA tasks only by fine-tuning a few images. Experiments on synthetic and authentic datasets proved that our approach has more vital learning ability and better generalization performance for evaluating real distorted images, effectively reducing dependence on manual marking.

Semiparametric single-index models for optimal treatment regimens with censored outcomes.

There is a growing interest in precision medicine, where a potentially censored survival time is often the most important outcome of interest. To discover optimal treatment regimens for such an outcome, we propose a semiparametric proportional hazards model by incorporating the interaction between treatment and a single index of covariates through an unknown monotone link function. This model is flexible enough to allow non-linear treatment-covariate interactions and yet provides a clinically interpretable linear rule for treatment decision. We propose a sieve maximum likelihood estimation approach, under which the baseline hazard function is estimated nonparametrically and the unknown link function is estimated via monotone quadratic B-splines. We show that the resulting estimators are consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. The optimal treatment rule follows naturally as a linear combination of the maximum likelihood estimators of the model parameters. Through extensive simulation studies and an application to an AIDS clinical trial, we demonstrate that the treatment rule derived from the single-index model outperforms the treatment rule under the standard Cox proportional hazards model.

Monitoring a Bernoulli process subject to gradual changes in the success rates of a sequence of Bernoulli random variables

We look at a sequence of Bernoulli random variables where the success rates change from θ 1 to θ 2. We will assume that both the success rates before and after the change are known. We also assume that this change does not happen abruptly but gradually over a period of time η where η is known. We calculate the probability that the change has started and completed. We also look at optimal stopping rules assuming that there is a cost for a false alarm and a cost per time unit to stop early.

Integrated optimization of design and production for personalized products

PurposeThe purpose of this paper is to study the profit optimization of manufacturers who provide personalized products to customers, thus avoiding additional operational costs and response times in the production process of personalized product design.Design/methodology/approachFirst, the authors present an integrated model for optimizing profit where the design and production of personalized products are both considered. The authors propose the concept of personalization level and four cases of personalization level are considered including top, high, medium and low levels. Polynomial-time optimal rules are provided for each level by analyzing 17 subcases where all possible personalized products are considered. Then, the authors compare the obtained profit of personalized products with the optimal profit of standardized products.FindingsAt low and high levels of personalization, personalized manufacturing is more profitable for specific products with specific characteristics. At the top and middle level of individuation, assuming that the product has certain characteristics, whether the best choice to produce personalized products depends on the level of individuation chosen by customers.Originality/valueAn important decision issue for manufacturers is whether to produce personalized or standardized products. In this study, the authors consider this decision problem and analyze the operational functions of personalized products. The authors hope to provide theoretical support in the operational management of manufacturers offering personalized products.

Building a well-balanced culture in the perioperative setting.

Healthcare institutions are currently under enormous financial, political and social pressure. Especially in the perioperative setting, various professional groups with differing agendas, dynamic teams, high-stress levels and diverging stakeholder interests are contributing to tension on a variety of levels. These players ask for guidance that goes beyond defined goals, clear structures or rules for process optimization. The impact of culture, which is influenced by core values, unspoken behaviours and practices, a shared purpose and implicit norms, has been often neglected. However, culture is a key factor in the search for optimal patient outcomes, quality of care, protection and long-time retention of staff, as well as economic success. In this review, we discuss important aspects to consider in building a great perioperative workplace, discuss indispensable adaptations in times of crisis and touch on urgently needed further investigations to optimize the art of developing, protecting, and cultivating a well-balanced culture.

An efficient approach for optimizing the cost-effective individualized treatment rule using conditional random forest.

Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective individualized treatment rule under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit to assess the trade-off between health benefits and related costs. We estimate cost-effective individualized treatment rule as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal cost-effective individualized treatment rule using net-monetary-benefit-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial to illustrate the cost-effectiveness gains of assigning customized intensive blood pressure therapy.

Computation Offloading and Service Caching for Mobile Edge Computing Under Personalized Service Preference

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mobile edge computing</i> (MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computation offloading</i> (CO). The other is service-specific data downloaded from a central cloud, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">service caching</i> (SC). It is noteworthy that CO and SC decisions are coupled when each user’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">service preference</i> (SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP’s result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm’s effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

Diagnostic Performance and Clinical Utility of Referral Rules to Identify Primary Care Patients at Risk of an Inflammatory Rheumatic Disease.

To determine the diagnostic performance and clinical utility of the Rotterdam Early Arthritis Cohort (REACH) and the Clinical Arthritis Rule (CARE) referral rules in an independent population of unselected patients from primary care. This study consisted of adults who were suspected of the need for referral to a rheumatologist by their general practitioner. Diagnostic accuracy measures and a net benefit approach were used to compare both rules to usual care for recognizing inflammatory arthritis and inflammatory rheumatic diseases (IRDs). Using the least absolute shrinkage and selection operator method and cross-validation we created an optimal prediction rule for IRD. This study consisted of 250 patients, of whom 42 (17%) were diagnosed with inflammatory arthritis and 55 (22%) with an IRD 3 months after referral. Considering inflammatory arthritis, the area under the receiver operating characteristic curve (AUC) was 0.72 (95% confidence interval [95% CI] 0.64-0.80) for REACH and 0.82 (95% CI 0.75-0.88) for CARE. Considering IRD, the AUC was 0.66 (95% CI 0.58-0.74) for REACH and 0.76 (95% CI 0.69-0.83) for CARE. CARE was of highest clinical value when compared to usual care. The composite referral rule for IRD of 10 parameters included sex, age, joint features, acute onset of symptoms, physical limitations, and duration of symptoms (AUC 0.82 [95% CI 0.75-0.88]). Both validated rules have a net benefit in recognizing inflammatory arthritis as well as IRD compared to usual care, but CARE shows superiority over REACH. Although the composite referral rule indicates a greater diagnostic performance, external validation is needed.

Datalog in Wonderland

Modern data analytics applications, such as knowledge graph reasoning and machine learning, typically involve recursion through aggregation. Such computations pose great challenges to both system builders and theoreticians: first, to derive simple yet powerful abstractions for these computations; second, to define and study the semantics for the abstractions; third, to devise optimization techniques for these computations. In recent work we presented a generalization of Datalog called Datalog, which addresses these challenges. Datalog is a simple abstraction, which allows aggregates to be interleaved with recursion, and retains much of the simplicity and elegance of Datalog. We define its formal semantics based on an algebraic structure called Partially Ordered Pre-Semirings, and illustrate through several examples how Datalog can be used for a variety of applications. Finally, we describe a new optimization rule for Datalog, called the FGH-rule, then illustrate the FGH-rule on several examples, including a simple magic-set rewriting, generalized semi-naïve evaluation, and a bill-of-material example, and briefly discuss the implementation of the FGH-rule and present some experimental validation of its effectiveness.

Optimal voting rules for international organizations, with an application to the United Nations

AbstractThis paper studies a self‐enforcing mechanism for an international organization that interacts repeatedly over time. A random shock determines which countries would be in favor of or against taking a collective action. If the organization wants to take the action, incentives must be provided. The optimal stationary equilibrium is equivalent to a mechanism characterized by voting weights. When the discount factor is not too high, the voting weights are random. Moreover, within a class of parameters, the optimal mechanism mimics the way voting power is distributed among United Nations members via Security Council seats. That is, the model rationalizes the existence of a council with permanent members who have veto power, nonpermanent members, and the exact number of permanent and nonpermanent members observed at the Security Council.

Sparse signal reconstruction via collaborative neurodynamic optimization

In this paper, we formulate a mixed-integer problem for sparse signal reconstruction and reformulate it as a global optimization problem with a surrogate objective function subject to underdetermined linear equations. We propose a sparse signal reconstruction method based on collaborative neurodynamic optimization with multiple recurrent neural networks for scattered searches and a particle swarm optimization rule for repeated repositioning. We elaborate on experimental results to demonstrate the outperformance of the proposed approach against ten state-of-the-art algorithms for sparse signal reconstruction.

Solving large-scale dynamic vehicle routing problems with stochastic requests

Dynamic vehicle routing problems (DVRPs) arise in several applications such as technician routing, meal delivery, and parcel shipping. We consider the DVRP with stochastic customer requests (DVRPSR), in which vehicles must be routed dynamically with the goal of maximizing the number of served requests. We model the DVRPSR as a multi-stage optimization problem, where the first-stage decision defines route plans for serving scheduled requests. Our main contributions are knapsack-based linear models to approximate accurately the expected reward-to-go, measured as the number of accepted requests, at any state of the stochastic system. These approximations are based on representing each vehicle as a knapsack with a capacity given by the remaining service time available along the vehicle’s route. We combine these approximations with optimal acceptance and assignment decision rules and derive efficient and high-performing online scheduling policies. We further leverage good predictions of the expected reward-to-go to design initial route plans that facilitate serving dynamic requests. Computational experiments on very large instances based on a real street network demonstrate the effectiveness of the proposed methods in prescribing high-quality offline route plans and online scheduling decisions.

Optimizing filter rule parameters with genetic algorithm and stock selection with artificial neural networks for an improved trading: The case of Borsa Istanbul

Filter rule along with other trading algorithms is used to identify potentially profitable trading points in stock markets. In this study, the scope of the filter rule has been expanded to include different moving average types. The filter rule parameters that will provide the highest return for each of the stocks listed in Borsa Istanbul have been optimized by using genetic algorithm. A number of 357 stocks traded in Borsa Istanbul is included in the dataset of the study between 06-07-2012 and 31-03-2020 period. To improve the poor performance in out-of-sample sets of optimal rules, the stock selection process was performed by means of artificial neural networks. The artificial neural network model predicts the performance of the stock in the test set by using the performance values in the training set. Results indicate that the returns of the selected stocks are significantly higher than the returns of the buy and hold strategy. Parameter optimization of filter rule with genetic algorithms and stock selection with the artificial neural networks can be used as a decision support system for investors, where they can make a profit above the market return. When only the genetic algorithm results are taken into account, it can be stated that Borsa Istanbul is a weak form efficient market. However, selecting the stocks with the assistance of artificial neural networks made it possible to obtain excess returns over the market.

CAPITAL: Optimal subgroup identification via constrainedpolicy tree search.

Personalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicine is to identify a subgroup of patients, based on baseline covariates, that benefits more from the targeted treatment than other comparative treatments. Most of the current subgroup identification methods only focus on obtaining a subgroup with an enhanced treatment effect without paying attention to subgroup size. Yet, a clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment. In this article, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre‐specified clinically meaningful mean outcome, such as the average treatment effect. We derive two equivalent theoretical forms of the optimal SSR based on the contrast function that describes the treatment‐covariates interaction in the outcome. We further propose a constrained policy tree search algorithm (CAPITAL) to find the optimal SSR within the interpretable decision tree class. The proposed method is flexible to handle multiple constraints that penalize the inclusion of patients with negative treatment effects, and to address time to event data using the restricted mean survival time as the clinically interesting mean outcome. Extensive simulations, comparison studies, and real data applications are conducted to demonstrate the validity and utility of our method.

Performance of the Machine Learning on Controlling the Pneumatic Suspension of Automobiles on the Rigid and Off-Road Surfaces

To enhance the ride comfort and control performance of the semi-active pneumatic suspension system (PSS) of automobiles on the different road surfaces, a machine learning method (MLM) developed on the optimal control rules of the fuzzy logic control is proposed for the semi-active PSS. A nonlinear dynamic model of the automobile with eight degrees of freedom (DOF) is established to compute the results. The root mean square (RMS) accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile are selected to evaluate the ride comfort of the automobile on the rigid road and off-road terrain surfaces. The research results show that the off-road terrain surfaces remarkably affect the ride comfort of the automobile, especially at a high moving speed range of the automobile over 17.5 m/s. The performance of the MLM in improving the ride comfort of the automobile is better than the fuzzy logic control under various simulation conditions. Particularly, the RMS accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile with the MLM are smaller than that of the fuzzy logic control by 14.6%, 9.6%, and 5.3% on the rigid road surfaces and reduced by 14.9%, 8.7%, and 9.8% on the soil terrain of off-road terrain surfaces, respectively. However, the research results also indicate that the performance of the MLM significantly depends on the data map of the learning process. Thus, to further enhance the performance of the MLM, the data map for the machine learning process should be expanded under different operating conditions of the automobile.