Explore the vast range of titles available.

The purpose of this paper was to confirm the basic assumption that classification models are suitable for solving the problem of data set classifications. We selected four representative models: BaiesNet, NaiveBaies, MultilayerPerceptron, and J48, and applied them to a four-class classification of a specific set of hepatitis C virus data for Egyptian patients. We conducted the study using the WEKA software classification model, developed at Waikato University, New Zealand. Defeat results were obtained. None of the four classes envisaged has been determined reliably. We have described all 16 metrics, which are used to evaluate classification models, listed their characteristics, mutual differences, and the parameter that evaluates each of these metrics. We have presented comparative, tabular values that give each metric for each classification model in a concise form, detailed class accuracy with a table of best and worst metric values, confusion matrices for all four classification models, and a type I and II error table for all four classification models. In addition to the 16 metric classifications, which we described, we listed seven other metrics, which we did not use because we did not have the opportunity to show their application on the selected data set. Metrics were negatively rated selected, standard reliable, classification models. This led to the conclusion that the data in the selected data set should be pre-processed to be reliably classified by the classification model.

In most classification tasks, there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framework for multiclass set-valued classification, where the classifiers guarantee user-defined levels of coverage or confidence (the probability that the true label is contained in the set) while minimizing the ambiguity (the expected size of the output). We first derive oracle classifiers assuming the true distribution to be known. We show that the oracle classifiers are obtained from level sets of the functions that define the conditional probability of each class. Then we develop estimators with good asymptotic and finite sample properties. The proposed estimators build on existing single-label classifiers. The optimal classifier can sometimes output the empty set, but we provide two solutions to fix this issue that are suitable for various practical needs. Supplementary materials for this article are available online.

Human activities pose a significant threat to the water quality of rivers when pollution exceeds the threshold limit. Urban activities in particular are highlighted as one of the major causes of contamination in surface water bodies in Asian countries. Evaluation of sustainable human population capacities in river watersheds is necessary to maintain better freshwater ecosystems in a country while achieving its development goals as a nation. We evaluated the correlation between the growth rate of the population in a watershed area and water quality parameters of a river ecosystem. The Kelani River in Sri Lanka was selected for the study. The highest correlation coefficients of 0.7, 0.69, 0.69 (p < 0.01) corresponding to biochemical oxygen demand (BOD), dissolved oxygen (DO) and total coliform (TC) were obtained with the population in watersheds of the Kelani river in Sri Lanka. Thus, we propose a quantitative approach to estimating the population capacity of watersheds based on water quality classification standards (WQCS), employing the Bayesian network (BN) classification model. The optimum population ranges were obtained from the probability distribution table of the population node in the BN. The results showed that the population density should be approximately less than 2375 to keep the water quality in the watershed for bathing and drinking purposes and approximately less than 2672 for fish and other aquatic organisms. This research will offer a means that can used to understand the impact of population on water quality in river basins and confer direct influence on natural water bodies.

Introduction Preoperative differentiation of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) using computed tomography (CT) is crucial for clinical management. However, accurately classifying pure ground-glass nodules (pGGNs) presents significant challenges. The quantitative integration of intratumor heterogeneity (ITH) scores may enhance the accuracy of this ternary classification. Therefore, this study aimed to develop ternary classification models to classify AIS, MIA, and IAC by leveraging insights from 15 machine-learning algorithms and integrating ITH scores with clinical data. Methods The ternary classification models were evaluated using an independent validation set to assess metrics, such as the macro-average area under the curve (AUC), accuracy, precision, recall, and F1 score. We subsequently applied binary classification models to various tasks derived from the optimal ternary classification model to sequentially address the discordant classifications. Results In this retrospective study, a total of 512 potential pGGNs were classified into training and validation sets at a ratio of 7:3. Among the 15 models, the light gradient boosting machine (LightGBM) exhibited the best predictive performance as a ternary classification model, achieving a macro-average AUC and an accuracy of 0.808 and 0.630, respectively. Upon binary classification, the model achieved a respective AUC and accuracy of 0.839 and 0.630 for classifying AIS, 0.677 and 0.620 for classifying MIA, and 0.908 and 0.780 for classifying IAC. Conclusion The LightGBM model, identified as the optimal algorithm for integrating ITH scores with clinical data, effectively serves as a ternary classification model for assessing adenocarcinoma invasiveness on chest CT.

In recent decades, there has been a growing interest in comparative studies about migrant integration, assimilation and the evaluation of policies implemented for these purposes. Over the years, the Migrant Integration Policy Index (MIPEX) has become a reference on these topics. This index measures and evaluates the policies of migrants’ integration in 52 countries over time. However, the comparison of very different countries can be difficult and, if not well conducted, can lead to misleading interpretations and evaluations of the results. The aim of this paper is to improve this comparison and facilitate the reading of the considered phenomenon, by applying a Mixture of Matrix-Normals classification model for longitudinal data. Focusing on data for 7 MIPEX dimensions from 2014 to 2019, our analysis identify 5 clusters of countries, facilitating the evaluation and the comparison of the countries within each cluster and between different clusters.

We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors.

We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.

Audio features are physical features that reflect single or complex coordinated movements in the vocal organs. Hence, in speech-based automatic depression classification, it is critical to consider the relationship among audio features. Here, we propose a deep learning-based classification model for discriminating depression and its severity using correlation among audio features. This model represents the correlation between audio features as graph structures and learns speech characteristics using a graph convolutional neural network. We conducted classification experiments in which the same subjects were allowed to be included in both the training and test data (Setting 1) and the subjects in the training and test data were completely separated (Setting 2). The results showed that the classification accuracy in Setting 1 significantly outperformed existing state-of-the-art methods, whereas that in Setting 2, which has not been presented in existing studies, was much lower than in Setting 1. We conclude that the proposed model is an effective tool for discriminating recurring patients and their severities, but it is difficult to detect new depressed patients. For practical application of the model, depression-specific speech regions appearing locally rather than the entire speech of depressed patients should be detected and assigned the appropriate class labels.

The article presents an Emerging Farmer Classification model and its typologies, revealing the dominance of medium-scale farmers, consisting of smallholder and medium-sized farms, in Hwedza district. The article argues that the Emerging Farmer Classification reflects ongoing reconfiguration in Hwedza district and is a result of the changing workings of capital after the Fast Track Land Reform era. The analysis is based on a case study involving 230 household interviews across five settlement models, and 20 in-depth interviews. The article identifies capital as a key driver shaping agrarian relations, following land redistribution in Zimbabwe.

An ever-increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and survey a variety of applications.