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"Ranking and selection (Statistics) Data processing."
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Learning and decision-making from rank data
The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state-of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.
Book
Who’s #1?
A website's ranking on Google can spell the difference between success and failure for a new business. NCAA football ratings determine which schools get to play for the big money in postseason bowl games. Product ratings influence everything from the clothes we wear to the movies we select on Netflix. Ratings and rankings are everywhere, but how exactly do they work? Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses.
eBook
The adaptive functional piecewise ordered weighted averaging method and its application to pollutant concentration analysis
The evolving patterns of pollutant concentrations and their rigorous assessment are critical issues in contemporary environmental research and policy-making, with important practical implications for air quality management and regional pollution control. To better support such decisions, scientifically sound multi-criteria ranking methods have become a key research focus. In this paper, we propose a novel adaptive functional piecewise ordered weighted averaging (FP-OWA) method for ranking complex functional data. The method extends the existing functional piecewise ranking–weighting framework by integrating data smoothing, depth-based centrality measures, and rank-based aggregation. We systematically compare the performance of FP-OWA with several existing functional data ranking methods using Monte Carlo simulations. The results show that FP-OWA substantially improves ranking consistency and stability when the data are contaminated by white noise. We further apply FP-OWA to rank the daily average PM 2.5 and O 3 concentrations in 13 cities in the Beijing–Tianjin–Hebei region in 2023, accurately revealing the spatiotemporal differentiation patterns of regional pollution. These findings provide a solid technical basis for local governments to design pollution control strategies and improve air quality. Future research will focus on extending FP-OWA to highly nonlinear and complex functional data, further enhancing its computational efficiency to meet big-data processing requirements, and exploring additional application scenarios.
Journal Article
A Selective Review on Information Criteria in Multiple Change Point Detection
Change points indicate significant shifts in the statistical properties in data streams at some time points. Detecting change points efficiently and effectively are essential for us to understand the underlying data-generating mechanism in modern data streams with versatile parameter-varying patterns. However, it becomes a highly challenging problem to locate multiple change points in the noisy data. Although the Bayesian information criterion has been proven to be an effective way of selecting multiple change points in an asymptotical sense, its finite sample performance could be deficient. In this article, we have reviewed a list of information criterion-based methods for multiple change point detection, including Akaike information criterion, Bayesian information criterion, minimum description length, and their variants, with the emphasis on their practical applications. Simulation studies are conducted to investigate the actual performance of different information criteria in detecting multiple change points with possible model mis-specification for the practitioners. A case study on the SCADA signals of wind turbines is conducted to demonstrate the actual change point detection power of different information criteria. Finally, some key challenges in the development and application of multiple change point detection are presented for future research work.
Journal Article
Ranking the information content of distance measures
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Finding a small set of features that still retains sufficient information about the dataset is important for the successful application of many statistical learning approaches. We introduce a statistical test that can assess the relative information retained when using 2 different distance measures, and determine if they are equivalent, independent, or if one is more informative than the other. This ranking can in turn be used to identify the most informative distance measure and, therefore, the most informative set of features, out of a pool of candidates. To illustrate the general applicability of our approach, we show that it reproduces the known importance ranking of policy variables for Covid-19 control, and also identifies compact yet informative descriptors for atomic structures. We further provide initial evidence that the information asymmetry measured by the proposed test can be used to infer relationships of causality between the features of a dataset. The method is general and should be applicable to many branches of science.
Journal Article
APR-QKDN: A Quantum Key Distribution Network Routing Scheme Based on Application Priority Ranking
As the foundation of quantum secure communication, the quantum key distribution (QKD) network is impossible to construct by using the operation mechanism of traditional networks. In the meantime, most of the existing QKD network routing schemes do not fit some specific quantum key practicality scenarios. Aiming at the special scenario of high concurrency and large differences in application requirements, we propose a new quantum key distribution network routing scheme based on application priority ranking (APR-QKDN). Firstly, the proposed APR-QKDN scheme comprehensively uses the application’s priority, the total amount of key requirements, and the key update rate for prioritizing a large number of concurrent requests. The resource utilization and service efficiency of the network are improved by adjusting the processing order of requests. Secondly, the queuing strategy of the request comprehensively considers the current network resource situation. This means the same key request may adopt different evaluation strategies based on different network resource environments. Finally, the performance of the APR-QKDN routing scheme is compared with the existing schemes through simulation experiments. The results show that the success rate of application key requests of the APR-QKDN routing scheme is improved by at least 5% in the scenario of high concurrency.
Journal Article
Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment
This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (
0.1837, 0.183, 0.230, 0.276, 0.335
) for (
q
=
1, 3, 5, 7, 10
), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.
Journal Article
An Ensemble Feature Ranking Algorithm for Clustering Analysis
Feature ranking is a widely used feature selection method. It uses importance scores to evaluate features and selects those with high scores. Conventional unsupervised feature ranking methods do not consider the information on cluster structures; therefore, these methods may be unable to select the relevant features for clustering analysis. To address this limitation, we propose a feature ranking algorithm based on silhouette decomposition. The proposed algorithm calculates the ensemble importance scores by decomposing the average silhouette widths of random subspaces. By doing so, the contribution of a feature in generating cluster structures can be represented more clearly. Experiments on different benchmark data sets examined the properties of the proposed algorithm and compared it with the existing ensemble-based feature ranking methods. The experiments demonstrated that the proposed algorithm outperformed its existing counterparts.
Journal Article
A fuzzy computing approach to aggregate expert opinions using parabolic and exparabolic approximation procedures for solving multi-criteria group decision-making problems
Triangular fuzzy numbers (TFNs) are widely used for selection problems to determine expert opinions using linguistic expressions. Some aggregation procedures are developed to determine expert opinions more accurately. However, there is a need for a simple and more useful procedure to solve the selection problems more suitably. For this purpose, our study offers a triangular, exparabolic, and parabolic area calculation-based approximation approach for TFNs to aggregate the possible hedges (very and more or less) for TFNs. Hence, this aggregation procedure provides a tuning opportunity for classical TFN expressions to capture possible tuning processes to reflect the hesitancies of experts. The technique for order preferences by similarity to ideal solution (TOPSIS) method is applied in the two studies from extant literature, and suitable alternatives are determined as a result of the ranking process. Finally, a comparative analysis is presented to illustrate the efficiency of the proposed procedure. The conventional TOPSIS model’s ranking scores are very close for exemplified examples (i.e., 0.5308, 0.4510, 0.4550 and 0.5304, 0.4626, 0.4940), but the proposed model’s result has fluctuated for the same examples (i.e., 0.346, 0,669, 0,567 and 0.208, 0.991, 0.148). So, the main advantage of the proposed aggregation procedure is the alternative ranking scores separation capability analyzed with their linguistic diversification.
Journal Article
MOORA models based on new score function of interval-valued intuitionistic sets and apply to select materials for mushroom cultivation
Interval-valued intuitionistic fuzzy numbers (IVIFNs) were proposed by Atanassov since the 1980s. They have been applied to many practical problems. One of the problems posed when research is the ranking of interval-valued intuitionistic fuzzy numbers to apply it to decision-making problems. But so far, there has not been a common method to rank two arbitrary IVIFNs. In this paper, we propose a new ranking function based on polynomial and exponential functions to rank fuzzy numbers. The numerical examples are illustrated to show the effectiveness of our proposed score function. In this paper, the multi-criteria decision-making models based on our proposed new score function are also presented namely MOORA models. We apply that model to the selection of materials for growing king oyster mushrooms in Vietnam.
Journal Article