Ordinal regression based on learning vector quantization

We present a novel approach for assessing the reliability of error-prone systems and networks using an interpretable prototype-based classification framework. Specifically, reliability levels of consecutive k-out-of-n success systems, failure networks, and domination networks are classified using Generalized Matrix Learning Vector Quantization (GMLVQ). Beyond achieving accurate classification, the proposed method provides informative insights into the influence of input probabilities on the resulting reliability levels. To further enhance the interpretability of the learned relevance matrix in GMLVQ, we introduce two graph-based visualization strategies that reveal structural patterns and relevant feature interactions that are critical for reliability-level classification. The proposed approach is generally applicable to arbitrary coherent systems and can be further adapted to estimate the probability of a union of any finite family of events based on their individual and pairwise intersection probabilities.

Guidelines suggest performing urine steroid profiling in patients with indeterminate adrenal tumors to make a noninvasive diagnosis of adrenocortical carcinoma (ACC). However, urine steroid profiling is not widely available. To determine the accuracy of clinically available serum 11-deoxycortisol, 17OH-progesterone, and 17OH-pregnenolone in diagnosing ACC. We conducted a prospective single-center cohort study of patients with adrenal masses evaluated between 2015 and 2023. Serum was analyzed by liquid chromatography-mass spectrometry for 17OH-pregnenolone, 17OH-progesterone, and 11-deoxycortisol. Reference standard for adrenal mass included histopathology, imaging characteristics, imaging follow up of 2 years, or clinical follow up of 5 years. Localized Generalized Matrix Learning Vector Quantization analysis was used to develop serum steroid score and assessed with area under receiver operating curve. Of 263 patients with adrenal masses, 44 (16.7%) were diagnosed with ACC, 161 (61%) with adrenocortical adenomas (ACAs), 27 (10%) with other adrenal malignancies, and 31 (12%) with other. Hounsfield unit ≥ 20 was demonstrated in all ACCs, in all but 1 other adrenal malignancy, and in 58 (31%) ACAs. All 3 steroids were higher in patients with ACCs vs non-ACCs, including when comparing ACCs with functioning ACAs, and with ACAs with Hounsfield unit ≥ 20 (P < .0001 for all). Localized Generalized Matrix Learning Vector Quantization analysis yielded a serum steroid score that discriminated between ACC and non-ACC groups with a mean threshold fixed area under receiver operating curve of 0.823. We showed that measurements of 11-deoxycortisol, 17OH-progesterone, and 17OH-pregnenolone could be valuable in diagnosing ACC. After appropriate validation, serum steroid score could be integrated in clinical practice.

Less is known about the relationship between conduct disorder (CD), callous-unemotional (CU) traits, and positive and negative parenting in youth compared to early childhood. We combined traditional univariate analyses with a novel machine learning classifier (Angle-based Generalized Matrix Learning Vector Quantization) to classify youth (N = 756; 9-18 years) into typically developing (TD) or CD groups with or without elevated CU traits (CD/HCU, CD/LCU, respectively) using youth- and parent-reports of parenting behavior. At the group level, both CD/HCU and CD/LCU were associated with high negative and low positive parenting relative to TD. However, only positive parenting differed between the CD/HCU and CD/LCU groups. In classification analyses, performance was best when distinguishing CD/HCU from TD groups and poorest when distinguishing CD/HCU from CD/LCU groups. Positive and negative parenting were both relevant when distinguishing CD/HCU from TD, negative parenting was most relevant when distinguishing between CD/LCU and TD, and positive parenting was most relevant when distinguishing CD/HCU from CD/LCU groups. These findings suggest that while positive parenting distinguishes between CD/HCU and CD/LCU, negative parenting is associated with both CD subtypes. These results highlight the importance of considering multiple parenting behaviors in CD with varying levels of CU traits in late childhood/adolescence.

Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases nominal classification schemes will ignore the class order relationships, which can have detrimental effect on classification accuracy. This paper introduces a novel ordinal Learning Vector Quantization (LVQ) scheme, with metric learning, specifically designed for classifying data items into ordered classes. Unlike in nominal LVQ, in ordinal LVQ the class order information is utilized during training in selection of the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype based models are in general more amenable to interpretations and can often be constructed at a smaller computational cost than alternative non-linear classification models. Experiments demonstrate that the proposed ordinal LVQ formulation compares favorably with its nominal counterpart. Moreover, our method achieves competitive performance against existing benchmark ordinal regression models.

A New Discriminative Ordinal Regression Method

Education can be used to determine the standard quality of life. One way to get an education is studying in the schools. In Semarang, there are several schools, one of which is SMAN 2 Semarang. In order to pass the admission selection of students at SMAN 2 Semarang, students must fulfill the requirements that had specified by the school. To determine the factors that affect the acceptance of students, the author uses ordinal logistic regression method. Ordinal logistic regression method is used to model the relationship between the response variable that consists of more than two categories and there are levels in that category with several independent variables that are categories or continuous. After doing research using ordinal logistic regression method, the result is that the factors that affect the acceptance of students of SMAN 2 Semarang is Indonesian scores, English scores, Mathematics scores, Science scores, Benefit scores, Achievement scores and also Rayon with the accuracy of the classification by 89, 63%. Keywords: Education, Admission of Students, Ordinal Logistic Regression

This paper presents the deck-of-cards-based Ordinal Regression (DOR), a new multicriteria decision-aiding procedure that conjugates the deck-of-cards method with an ordinal regression approach to define a multicriteria value function representing the preferences of the decision maker (DM). The deck-of-cards method allows the DM to express the ranking order of a set of reference alternatives along with the intensity of preferences between reference alternatives. An ordinal regression procedure is then used to define a multicriteria value function that represents the ranking of the reference alternatives as well as the preference intensity. This approach can be applied to define value functions with different formulations, such as weighted sum, additive value, or Choquet integral. The value function thus obtained can be used to comprehensively evaluate alternatives of a multi-criteria decision problem. The value function provided by DOR can also be applied to a multi-objective optimisation problem. In this study, we applied DOR to handle urban and regional planning decisions in which facilities are required to be selected, located, and planned. In particular, we consider the interactions between criteria and synergies between facilities in an enriched version of the so-called space–time model. We applied this methodology to a real-world problem to plan the development of a sustainable ecovillage in the province of Turin (Italy), thus supporting the president of the cooperative owning the ecovillage in his decisions regarding which structures to select, where to locate them, and when to plan their realisation.

Robust and accurate modeling of motor vehicle accident and injury severities have significant impact on transportation safety and economy. The capability to assess accident risk based on external driving conditions (e.g., weather, road condition, etc.) and driver behavior and characteristics can reduce accident occurrences by alerting drivers to alleviated risk. In this paper, we propose a novel accident risk assessment framework driven by ordinal regression. One challenge of the risk assessment problem is that non-accident data are not collected by any agency in their study of transportation safety. Hence, we also propose a realistic negative data generation scheme based on feature weighs derived from multinomial logistic regression to overcome this challenge. Experimental results on two different real-world datasets from the US National Highway Traffic Safety Administration and UK Transport for Greater Manchester are used to demonstrate the feasibility and robustness of our proposed ordinal regression framework. Performance on four ordinal regression algorithms, namely: logistic all-threshold, logistic immediate-threshold, ordinal ridge, and least absolute deviations are compared. In addition, for US dataset, we investigate the effect of random oversampling and undersampling on the proposed risk assessment framework. We empirically show that bagging with random oversampling using logistic all-threshold ordinal regression method has the best prediction performance among ordinal regression models.

Accurately predicting user preferences/ratings over items are crucial for many Internet applications, e.g., recommender systems, online advertising. In current main-stream algorithms regarding the rating prediction problem, discrete rating scores are often viewed as either numerical values or(nominal) categorical labels. Practically, viewing user rating scores as numerical values or categorical labels cannot precisely reflect the exact degree of user preferences. It is expected that for each user, the quantitative distance/scale between any pair of adjacent rating scores could be different. In this paper, we propose a new ordinal regression approach. We view ordered preference scores in an additive way, where we are able to model users» internal rating patterns. Specifically, we model and learn the quantitative distances/scales between any pair of adjacent rating scores. In this way, we can generate a mapping from users» assigned discrete rating scores to the exact magnitude/degree of user preferences for items. In the application of rating prediction, we combine our newly proposed ordinal regression method with matrix factorization, forming a new ordinal matrix factorization method. Through extensive experiments on benchmark datasets, we show that our method significantly outperforms existing ordinal methods, as well as other popular collaborative filtering methods in terms of the rating prediction accuracy.

Deep learning-based methods have achieved promising performance in early\ndetection and classification of lung nodules, most of which discard unsure\nnodules and simply deal with a binary classification -- malignant vs benign.\nRecently, an unsure data model (UDM) was proposed to incorporate those unsure\nnodules by formulating this problem as an ordinal regression, showing better\nperformance over traditional binary classification. To further explore the\nordinal relationship for lung nodule classification, this paper proposes a meta\nordinal regression forest (MORF), which improves upon the state-of-the-art\nordinal regression method, deep ordinal regression forest (DORF), in three\nmajor ways. First, MORF can alleviate the biases of the predictions by making\nfull use of deep features while DORF needs to fix the composition of decision\ntrees before training. Second, MORF has a novel grouped feature selection (GFS)\nmodule to re-sample the split nodes of decision trees. Last, combined with GFS,\nMORF is equipped with a meta learning-based weighting scheme to map the\nfeatures selected by GFS to tree-wise weights while DORF assigns equal weights\nfor all trees. Experimental results on the LIDC-IDRI dataset demonstrate\nsuperior performance over existing methods, including the state-of-the-art\nDORF.\n

Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and identically distributed (IID) instances via formulating a single ordinal regression task. However, for heterogeneous non-IID instances with well-defined local geometric structures, e.g., subpopulation groups, multi-task learning (MTL) provides a promising framework to encode task (subgroup) relatedness, bridge data from all tasks, and simultaneously learn multiple related tasks in efforts to improve generalization performance. Even though MTL methods have been extensively studied, there is barely existing work investigating MTL for heterogeneous data with ordinal labels. We tackle this important problem via sparse and deep multi-task approaches. Specifically, we develop a regularized multi-task ordinal regression (MTOR) model for smaller datasets and a deep neural networks based MTOR model for large-scale datasets. We evaluate the performance using three real-world healthcare datasets with applications to multi-stage disease progression diagnosis. Our experiments indicate that the proposed MTOR models markedly improve the prediction performance comparing with single-task ordinal regression models.

This paper proposes a novel Ordinal Regression Multi-Task Learning (OR-MTL) framework to address challenges in multi-task diagnosis of PD in Gas-Insulated Switchgear (GIS). GIS PD diagnosis typically involves tasks such as discharge-type identification and severity assessment, which is essentially an ordinal regression problem facing challenges such as high label noise and inconsistent ranking of prediction outcomes. To address these challenges, the OR-MTL framework introduces two key innovations: a dynamic task-weighting strategy based on excess risk estimation, which mitigates the negative impact of label noise on multi-task learning weight allocation, and an ordinal regression loss function based on conditional probability, which ensures consistent prediction ranking through conditional probability chains. Experiments on GIS PD datasets demonstrate that the excess risk-based task-weighting strategy exhibits superior robustness compared to traditional methods in high-noise environments, while the proposed ranking consistency loss function significantly improves the accuracy of severity assessment and reduces errors. Ablation studies further validate the effectiveness of the complete OR-MTL framework. This research not only provides an efficient solution for GIS PD diagnosis but also offers new insights and methodologies for multi-task learning involving ordinal regression tasks.

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) inherent noise ispresent in the sensor readings. The two scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process, often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem and propose LSTM-OR: deep Long Short Term Memory (LSTM) network-based approach to learn the OR function. We show that LSTM-OR naturally allows for the incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effectiveapproach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on the publicly available turbofan engine benchmark datasets, we demonstrate that LSTMOR is at par with commonly used deep metric regressionbased approaches for RUL estimation when sufficient failed instances are available for training. Importantly, LSTM-OR outperforms these metric regression-based approaches in the practical scenario where failed training instances are scarce, but sufficient operational (censored) instances are additionally available. Furthermore, our uncertainty quantification approach yields high-quality predictive uncertainty estimateswhile also leading to improved RUL estimates compared to single best LSTM-OR models.

&lt;p&gt;The aims of this study was to analyze the kind of people willingness to pay and the factors affecting the existence of Etawah crossbreed goat group farm. Girikerto Village, Turi, Sleman was chosen as research site because it was the&lt;br /&gt;center of Etawah crossbreed goat village system in Sleman. Samples of 40 people at surrounding area were determined purposively based on residence distance to the village group system location. To analyze the relation between the willingness to pay of people and its affecting factors Ordinal Logit Regression Analysis methods were used with STATA software version 6.0. Aid in labor form has higher value than material physical aid. It was due to in social institution role of social capital in form of voluntary work very much influent the existence of village group system. Model estimation with Ordinal Logit Regression, indicated that education, total income, distance of residence to village group system, salak plantation area, and occupation type had positively significant effect on people willingness to pay, while based on marginal effect calculation, all independent variables indicated negative tendency or low appreciation from people toward environment.&lt;/p&gt;&lt;p&gt;(Key words: The willingness to pay of people, Ordinal Logit, Social capital, Marginal effect)&lt;br /&gt;&lt;br /&gt;&lt;/p&gt;

BackgroundStandardised mortality ratios do not provide accurate measures of preventable mortality. This has generated interest in using case notes to assess the preventable component of mortality. But, different methods of...