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Journal articles on the topic 'Classifiers'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Amerineni, Rajesh, Resh S. Gupta, and Lalit Gupta. "Multimodal Object Classification Models Inspired by Multisensory Integration in the Brain." Brain Sciences 9, no. 1 (2019): 3. http://dx.doi.org/10.3390/brainsci9010003.

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Two multimodal classification models aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli are introduced. The feature-integrating model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The decision-integrating model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classi

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Li, Gang, Mengdi Shen, Meixuan Li, and Jingyi Cheng. "Personal Credit Default Discrimination Model Based on Super Learner Ensemble." Mathematical Problems in Engineering 2021 (March 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/5586120.

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Assessing the default of customers is an essential basis for personal credit issuance. This paper considers developing a personal credit default discrimination model based on Super Learner heterogeneous ensemble to improve the accuracy and robustness of default discrimination. First, we select six kinds of single classifiers such as logistic regression, SVM, and three kinds of homogeneous ensemble classifiers such as random forest to build a base classifier candidate library for Super Learner. Then, we use the ten-fold cross-validation method to exercise the base classifier to improve the base

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Song, Chongya, Alexander Pons, and Kang Yen. "Sieve: An Ensemble Algorithm Using Global Consensus for Binary Classification." AI 1, no. 2 (2020): 242–62. http://dx.doi.org/10.3390/ai1020016.

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In the field of machine learning, an ensemble approach is often utilized as an effective means of improving on the accuracy of multiple weak base classifiers. A concern associated with these ensemble algorithms is that they can suffer from the Curse of Conflict, where a classifier’s true prediction is negated by another classifier’s false prediction during the consensus period. Another concern of the ensemble technique is that it cannot effectively mitigate the problem of Imbalanced Classification, where an ensemble classifier usually presents a similar magnitude of bias to the same class as i

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Gaikwad, D. P. "Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors." International Journal of Computer Network and Information Security 13, no. 4 (2021): 26–34. http://dx.doi.org/10.5815/ijcnis.2021.04.03.

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Recently, the use of Internet is increased for digital communication to share a lot of sensitive information between computers and mobile devices. For secure communication, data or information must be protected from adversaries. There are many methods of safeties like encryption, firewalls and access control. Intrusion detection system is mainly used to detect internal attacks in organization. Machine leaning techniques are mostly used to implement intrusion detection system. Ensemble method of machine learning gives high accuracy in which moderately accurate classifiers are combined. Ensemble

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Assumpção Silva, Ronan, Alceu S. Britto, Fabricio Enembreck, Robert Sabourin, and Luiz S. Oliveira. "Selecting and Combining Classifiers Based on Centrality Measures." International Journal on Artificial Intelligence Tools 29, no. 03n04 (2020): 2060004. http://dx.doi.org/10.1142/s0218213020600040.

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Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier’

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Kumar, Amit, and Anand Shanker Tewari. "Risk Identification of Diabetic Macular Edema Using E-Adoption of Emerging Technology." International Journal of E-Adoption 14, no. 3 (2022): 1–20. http://dx.doi.org/10.4018/ijea.310000.

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The accumulation of the blood leaks on the retina is known as diabetic macular edema (DME), which can result in irreversible blindness. Early diagnosis and therapy can stop DME. This study presents an e-adoption of emerging technology such as RadioDense model for detecting and classifying DME from retinal fundus images. The proposed model employs a modified version of DenseNet121, radiomics features, and the gradient boosting classifier. The authors evaluated many classifiers on the concatenated features. The efficacy of the classifier is determined by comparing each classifier's accuracy valu

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Chuah, Joshua, Uwe Kruger, Ge Wang, Pingkun Yan, and Juergen Hahn. "Framework for Testing Robustness of Machine Learning-Based Classifiers." Journal of Personalized Medicine 12, no. 8 (2022): 1314. http://dx.doi.org/10.3390/jpm12081314.

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There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers’ performance and changes in the classifiers’ parameter values using fact

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Zaidi, Ahmad Zairi, and Chun Yong Chong. "A Dynamic Selection Method for Touch-Based Continuous Authentication On Mobile Devices." Applied Mathematics and Computational Intelligence (AMCI) 13, no. 3 (2024): 26–65. http://dx.doi.org/10.58915/amci.v13i3.554.

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Touch biometric is one of the promising modalities to realise continuous authentication (CA) on mobile devices by distinguishing between touch strokes performed by the legitimate and illegitimate users. While the benefit of the scheme is promising, the effectiveness of different classification methods is not thoroughly understood. Little consideration has been given to dynamic selection of classifiers. In this paper, we proposed a dynamic selection method to deal with the security and usability needs of touch-based CA. Instead of classifying all touch samples using the same classifier, the met

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Paillassa, M., E. Bertin, and H. Bouy. "MAXIMASK and MAXITRACK: Two new tools for identifying contaminants in astronomical images using convolutional neural networks." Astronomy & Astrophysics 634 (February 2020): A48. http://dx.doi.org/10.1051/0004-6361/201936345.

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In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are able to identify various contaminants, such as cosmic rays, hot and bad pixels, persistence effects, satellite or plane trails, residual fringe patterns, nebulous features, saturated pixels, diffraction spikes, and tracking errors in images. They encompass a broad range of ambient conditions, such as seeing, image sampling, detector type, optics, and stellar density. The first classifier, MAXIMASK, performs semantic segmentation and generate

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Zou, Jiangbo, Xiaokang Fu, Lingling Guo, Chunhua Ju, and Jingjing Chen. "Creating Ensemble Classifiers with Information Entropy Diversity Measure." Security and Communication Networks 2021 (May 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/9953509.

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Ensemble classifiers improve the classification accuracy by incorporating the decisions made by its component classifiers. Basically, there are two steps to create an ensemble classifier: one is to generate base classifiers and the other is to align the base classifiers to achieve maximum accuracy integrally. One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. In this paper, we propose an ensemble classifier generating algorithm to improve the accuracy of an ensemble classification and to maximize the diversity o

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Chellappan, Dinesh, and Harikumar Rajaguru. "Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data." Biomimetics 8, no. 6 (2023): 503. http://dx.doi.org/10.3390/biomimetics8060503.

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In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear R

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Bajenaid, Aboubakr, Maher Khemakhem, Fathy E. Eassa, et al. "Towards Robust SDN Security: A Comparative Analysis of Oversampling Techniques with ML and DL Classifiers." Electronics 14, no. 5 (2025): 995. https://doi.org/10.3390/electronics14050995.

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Software-defined networking (SDN) is becoming a predominant architecture for managing diverse networks. However, recent research has exhibited the susceptibility of SDN architectures to cyberattacks, which increases its security challenges. Many researchers have used machine learning (ML) and deep learning (DL) classifiers to mitigate cyberattacks in SDN architectures. Since SDN datasets could suffer from class imbalance issues, the classification accuracy of predictive classifiers is undermined. Therefore, this research conducts a comparative analysis of the impact of utilizing oversampling a

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Pan, Chia-Jung. "Numeral classifiers in the Austronesian languages of Taiwan." Classifiers 3, no. 2 (2023): 153–80. http://dx.doi.org/10.1075/alal.22017.pan.

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Abstract This paper is a typological exploration of the nominal classification systems found in the Austronesian languages of Taiwan, with special focus on their meanings, forms and functions within the Austronesian typological context. Based on Aikhenvald’s defining properties and classifications of classifiers (2000, 2017, 2019, 2021, forthcoming), the Austronesian languages of Taiwan investigated in this study display a set of relatively small classifier systems consisting of numeral classifiers and other classifier-like terms, such as measure words and verbal action classifiers. Numeral cl

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Gupta, Yogesh K., and Khushboo Saxena. "An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier." International Journal of Electrical and Electronics Research 10, no. 4 (2022): 932–38. http://dx.doi.org/10.37391/ijeer.100430.

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Image Restoration is a significant phase to process images for their enhancement. Underwater photographs are subject to quality issues such as blurry photos, poor contrast, uneven lighting, etc. Image processing is crucial in the processing of these degraded images. This research introduced an ensemble-based classifier based on the bagging approach to enhance UW images. The support vector machine and random forest classifiers serve as the ensemble classifier's main classifiers. Additionally, to complement the feature optimization technique, the proposed ensemble classifier leverages particle s

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Kishore, U., and R. Ramadevi. "Analysis and Comparison of Kidney Stone Detection using Gaussian Maximum Likelihood Classifier and Bayesian Classifier with Improved Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 799–805. http://dx.doi.org/10.18137/cardiometry.2022.25.799805.

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Aim: The aim of this study is to figure out how to predict and detect kidney stones using Gaussian maximum likelihood classifier and Bayesian classifier and also to compare the results of classification accuracy between Gaussian maximum Likelihood classifier and Bayesian classifier. Materials and Methods: This study data was gathered via the kaggle website. Samples were considered as (N=10) for gaussian maximum likelihood classifier and (N=10) for bayesian classifier according to clinicalc.com, by keeping alpha error-threshold value 0.01, enrollment ratio as 0.1, 95% confidence interval, G pow

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Volkodav, Vladimir A., and Ivan A. Volkodav. "Development of the structure and composition of a building information classifier towards the application of BIM technologies." Vestnik MGSU, no. 6 (June 2020): 867–906. http://dx.doi.org/10.22227/1997-0935.2020.6.867-906.

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Abstract Introduction. Various building information classification systems are used internationally; their critical analysis makes it possible to highlight basic requirements applicable to the Russian classifier and substantiate its structure and composition. Materials and methods. Modern international building information classification systems, such as OmniClass (USA), Uniclass 2015 (UK), CCS (Denmark), and CoClass (Sweden), are considered in the article. Their structure, composition, methodological fundamentals are analyzed. In addition to international classification systems, R

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Ranjitha, R. G., and Sundaram A. Meenakshi. "Phishing Websites Classification Placed on URL Features and Extreme Machine Learning." Transactions on Federated Engineering and Systems 1, no. 1 (2023): 37–43. https://doi.org/10.5281/zenodo.10279674.

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Phishing attacks have become an increasingly common threat to individuals and organizations alike. The traditional methods used to detect phishing attacks, such as blacklisting known phishing URLs or using heuristics to identify suspicious websites have proven to be limited in their effectiveness. Phishing attackers continuously evolve their tactics, making it difficult for traditional detection methods to keep up. To address this challenge, this study explores the use of machine learning classifiers to uncover illegitimate websites. Specifically, this research utilizes the Multilayer Perceptr

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Yi, Liu, Diao Xing-chun, Cao Jian-jun, Zhou Xing, and Shang Yu-ling. "A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4953280.

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In order to improve utilization rate of high dimensional data features, an ensemble learning method based on feature selection for entity resolution is developed. Entity resolution is regarded as a binary classification problem, an optimization model is designed to maximize each classifier’s classification accuracy and dissimilarity between classifiers and minimize cardinality of features. A modified multiobjective ant colony optimization algorithm is employed to solve the model for each base classifier, two pheromone matrices are set up, weighted product method is applied to aggregate values

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Wang, Yan, Xiu Xia Wang, and Sheng Lai. "A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 55–58. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.55.

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In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity me

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Maddala, Jeevan Babu, Bhargav Reddy Modugulla, Sahithi Amulya Pulusu, Sanjay Mannepalli, Praveen prakash Pamidimalla, and Rukhiya Khanam. "Heart Failure Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1901–11. http://dx.doi.org/10.22214/ijraset.2024.59236.

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Abstract: Cardiovascular Disease (CVD) currently stands as the leading cause of death worldwide. Clinical data analytics encounter a significant challenge in accurately predicting cardiac disease. The healthcare industry generates vast volumes of raw data, necessitating its transformation into meaningful insights through machine learning techniques. The objective is to leverage machine learning models to improve the predictability of survival among cardiac patients. This study employs machine learning classifiers: Random Forest, Gradient Boosting classifier, Extra Tree Classifier, XG-Boost, Ad

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Liu, Chen-Sheng Luther. "Amount classifiers in Chinese and the mass/count distinction." International Journal of Chinese Linguistics 2, no. 1 (2015): 1–32. http://dx.doi.org/10.1075/ijchl.2.1.01liu.

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In Chinese, temporary classifiers form together with classifiers like piàn ‘piece’, tuán ‘something ball-shaped’, gǔ ‘stream or stand’ and pài ‘faction or clique’ a type of classifiers that has both mass and count forms. When occurring with the determinative quantifier yī to create a structure conveying the part-related reading, they appear in the ‘mass form’ (i.e., amount classifiers), but the ‘count form’ (i.e., number classifiers) when occurring with a genuine numeral. That is, the nominal mass/count distinction in Chinese is reflected at the classifier level as the distinction between the

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Zhang, Liang Xiu, Xu Yun Qiu, Rong Chun Guo, and Zhu Lin Zhang. "Research on Driving Fatigue Monitoring Based on Tree-Structure Classifier." Advanced Materials Research 490-495 (March 2012): 1506–10. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1506.

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The driving fatigue monitoring system based on tree structure classifier is presented. The realtime fatigue monitoring system is established by training face and eye tree-structure classifiers which consists of many strong classifiers. Each strong classifier choose several weak classifiers by AdoBoost recursive method and each weak classifier is characterized by single Haar-like features. The whole Tree-structure is constructed by clustering branches recursive algorithm. Experiment has shown that the driving fatigue monitoring system based on tree-structure classifiers is very reliable. Introd

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Lei, Lei, and Yafei Song. "Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification." Computational Intelligence and Neuroscience 2021 (October 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/5583031.

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Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to the metasubclasses which is used to learn the classifier. How to reduce the error caused by the noncompetent classifiers under diversity big enough is urgent for ECOC classification. The weighted decoding strategy can be used to reduce the error caused by the noncompetence contradiction through rel

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Greer, Kieran. "Category Trees - Classifiers that Branch on Category." International Journal of Artificial Intelligence & Applications 12, no. 06 (2021): 65–76. http://dx.doi.org/10.5121/ijaia.2021.12606.

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This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change

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NATH, RAJIV KUMAR. "FINGERPRINT RECOGNITION USING MULTIPLE CLASSIFIER SYSTEM." Fractals 15, no. 03 (2007): 273–78. http://dx.doi.org/10.1142/s0218348x07003605.

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In this paper, the human fingerprint, which is independent of rotation and scaling, is recognized. The multiple classification technique, based on wavelet and fractal analysis, is used. It is shown that systematic incorporation of decision from various classifiers leads to a better decision rather than simply fusing them. Multiple classifiers can serve as a means of enhancing the performance of pattern recognition problems. Multiple classifier system design involves the problem of classifier fusion. This paper deals with multi-classifier systems in which each classifier uses its own representa

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Saha, Abhijit, Jayasri Reddy, and Rishikesh Kumar. "A Fuzzy Similarity Based Classification with Archimedean-Dombi Aggregation Operator." Journal of Intelligent Management Decision 1, no. 2 (2022): 118–27. http://dx.doi.org/10.56578/jimd010205.

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The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed. The major goal of this study is to provide a classification algorithm that is effective and accurate. In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier. Earlier, Dombi operators were used to study the similarity classifier. We focus on the application of Archimedea

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Aikhenvald, Alexandra Y. "Classifiers: Setting the scene." Classifiers 3, no. 2 (2023): 141–52. http://dx.doi.org/10.1075/alal.22022.aik.

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Abstract Classifiers are morphemes which occur under specifiable conditions and which categorise nominal referents in terms of their animacy, shape, and other properties. The most widely represented type is numeral classifiers, which occur next to a number word or a quantifier. Further types include noun classifiers, verbal classifiers, classifiers in possessive constructions, and deictic classifiers. One language can have more than one type of classifier. In some, the same set of classifiers occurs in several classifier contexts, corroborating the unity of the phenomenon. Classifiers categori

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Astariani, Sandra. "A Comparative Study of Indonesian and Japanese Classifiers." Linguistika: Buletin Ilmiah Program Magister Linguistik Universitas Udayana 30, no. 2 (2023): 113. http://dx.doi.org/10.24843/ling.2023.v30.i02.p05.

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Abstract  Classifiers belong to open class noun. All languages are naturally occupied with classifiers, yet the usage is various depending on how the language treats them. Japanese language, as an obligatory-classifier language, makes the classifiers compulsory, in contrast with an optional-classifier language, Indonesian language. Despite this, Japanese and Indonesian languages treat the classifiers syntactically and semantically similar. This article aims at revealing, both the semantic and syntactic differences and similarities between Japanese and Indonesian classifiers. The analysis

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Ahmad, Amir, Hamza Abujabal, and C. Aswani Kumar. "Random Subclasses Ensembles by Using 1-Nearest Neighbor Framework." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (2017): 1750031. http://dx.doi.org/10.1142/s0218001417500318.

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A classifier ensemble is a combination of diverse and accurate classifiers. Generally, a classifier ensemble performs better than any single classifier in the ensemble. Naive Bayes classifiers are simple but popular classifiers for many applications. As it is difficult to create diverse naive Bayes classifiers, naive Bayes ensembles are not very successful. In this paper, we propose Random Subclasses (RS) ensembles for Naive Bayes classifiers. In the proposed method, new subclasses for each class are created by using 1-Nearest Neighbor (1-NN) framework that uses randomly selected points from t

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PALUBINSKAS, GINTAUTAS. "A COMPARATIVE STUDY OF DECISION MAKING ALGORITHMS IN IMAGES MODELED BY GAUSSIAN MARKOV RANDOM FIELDS." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 04 (1988): 621–39. http://dx.doi.org/10.1142/s021800148800039x.

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In this work the performance and computer time requirements of 15 classifiers are compared in images modeled by two-dimensional Gaussian Markov random fields which are represented by a causal autoregressive model of the second order. The per-pixel classifier and the object classifier directly or indirectly utilizing spectral-spatial characteristies of images are among them. The probability of misclassification (PMC) calculated analytically and experimentally on modeled data was used as a measure of a classifier performance. The influence of such factors as the object size and form, the inadequ

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Jenks, Peter. "Evidence for the Syntactic Diversity of Numeral Classifiers." LSA Annual Meeting Extended Abstracts 1 (May 2, 2010): 15. http://dx.doi.org/10.3765/exabs.v0i0.494.

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I propose a syntactic typology of numeral classifier languages based on the observation that the order of nouns and numeral classifiers correlate with substantial differences in the syntactic uses of classifiers. In languages where classifiers precede nouns, they have an article-like use marking definiteness. We take this as evidence that classifiers are functional projections of the NP there, forming a constituent with that noun. In languages where classifiers follow nouns, they do not form a constituent with the head noun, explaining why these languages almost universally exhibit quantifier

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Vergara, Luis, and Addisson Salazar. "On the Optimum Linear Soft Fusion of Classifiers." Applied Sciences 15, no. 9 (2025): 5038. https://doi.org/10.3390/app15095038.

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We present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error improvement factor relative to the best fused classifier. Key elements for this improvement factor are the number of classifiers, their pairwise correlations, the imbalance between their performances, and the bias. Furthermore, we consider exponential models for the class-conditional probability dens

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Yen, Nguyen Thi Hai. "A Comparative Study of the Chinese Classifier "ZHI" and the Vietnamese Classifier "CON"." Journal of Literature and Arts Research 2, no. 2 (2025): 20–25. https://doi.org/10.71222/m9vdrh60.

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The Chinese classifier "ZHI" and the Vietnamese classifier "CON" are both quantifiers that denote units of objects. These two classifiers share several similarities, such as their frequent use with animal nouns and their formation of "number + classifier + noun" structures. Due to their resemblance, Vietnamese learners often encounter difficulties and make various errors when learning the Chinese classifier "ZHI". These errors result not only from native language interference but also from the inherent complexity of the classifier "ZHI" itself. This paper conducts a comparative analysis of the

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Luo, Yongxian. "Nominal classification in Zhuang." Classifiers 3, no. 2 (2023): 267–98. http://dx.doi.org/10.1075/alal.22011.luo.

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Abstract The Zhuang language employs a system of nominal classification, including numeral classifiers, modifier classifiers, class terms, kin prefixes, gender markers, prefixable morphemes for topographic names, among others. Quite often, the same set of morphemes is used as numeral classifiers and with demonstratives. The numeral classifier system is particularly salient, comprising numeral terms and noun classifiers, with dozens of classifiers with fine semantic distinctions. The modifier classifier system involves the use of classifiers in nominal modification of all kinds. The kinship sys

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Huang, Yang. "Classifiers in nDrapa." Classifiers 3, no. 2 (2023): 202–38. http://dx.doi.org/10.1075/alal.22009.hua.

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Abstract This paper introduces noun categorization devices in nDrapa. It argues that nDrapa usually requires both numeral classifiers and verbal action classifiers to express classificatory techniques. Morphologically, classifiers are fused with numerals; without the involvement of classifiers, numerals cannot be directly associated with nouns. Classifiers can derive from the pragmatic function, in which a considerable number of classifier-derived quantifiers and adverbs reveal the adverbial function with respect to the softening voice and politeness. Historically, the majority of classifiers

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Jumabay, Uldanay, Irina Nevskaya, and Saule Tazhibayeva. "Numeral classifiers in Kazakh." Classifiers 3, no. 2 (2023): 299–329. http://dx.doi.org/10.1075/alal.22024.jum.

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Abstract Many languages distinguish numeral classifiers in their grammar systems categorizing nominals in terms of their inherent nature, such as animacy, shape, form, and arrangement. Kazakh, a Turkic language of Central Asia, distinguishes numeral classifiers, which are used in numeral classifier constructions consisting of a numeral, a numeral classifier and a specified noun. Numeral classifiers in such constructions can be optional. This paper aims at a preliminary description of the features, types, and usage of numeral classifiers in Kazakh.

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Panigrahi, Ranjit, Samarjeet Borah, Akash Kumar Bhoi, et al. "Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research." Mathematics 9, no. 6 (2021): 690. http://dx.doi.org/10.3390/math9060690.

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Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessin

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Pyne, Yvette, Yik Ming Wong, Haishuo Fang, and Edwin Simpson. "Analysis of ‘One in a Million’ primary care consultation conversations using natural language processing." BMJ Health & Care Informatics Online 30, no. 1 (2023): e100659. http://dx.doi.org/10.1136/bmjhci-2022-100659.

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BackgroundModern patient electronic health records form a core part of primary care; they contain both clinical codes and free text entered by the clinician. Natural language processing (NLP) could be employed to generate these records through ‘listening’ to a consultation conversation.ObjectivesThis study develops and assesses several text classifiers for identifying clinical codes for primary care consultations based on the doctor–patient conversation. We evaluate the possibility of training classifiers using medical code descriptions, and the benefits of processing transcribed speech from p

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Jiang, Hongbo, and Yumin Chen. "Neighborhood Granule Classifiers." Applied Sciences 8, no. 12 (2018): 2646. http://dx.doi.org/10.3390/app8122646.

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Classifiers are divided into linear and nonlinear classifiers. The linear classifiers are built on a basis of some hyper planes. The nonlinear classifiers are mainly neural networks. In this paper, we propose a novel neighborhood granule classifier based on a concept of granular structure and neighborhood granules of datasets. By introducing a neighborhood rough set model, the condition features and decision features of classification systems are respectively granulated to form some condition neighborhood granules and decision neighborhood granules. These neighborhood granules are sets; thus,

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Ma, Weiyi, Peng Zhou, and Roberta Michnick Golinkoff. "The Role Classifiers Play in Selecting the Referent of a Word." Languages 8, no. 1 (2023): 84. http://dx.doi.org/10.3390/languages8010084.

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An important cue to the meaning of a new noun is its accompanying classifier. For example, in English, X in “a sheet of X” should refer to a broad, flat object. A classifier is required in Chinese to quantify nouns. Using children’s overt responses in an object/picture selection task, past research found reliable semantic knowledge of classifiers in Mandarin-reared children at around age three. However, it is unclear how children’s semantic knowledge differs across different types of classifiers and how this difference develops with age. Here we use an arguably more sensitive measure of childr

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Zhang, Fuyong, Kuan Li, and Ziliang Ren. "Improving Adversarial Robustness of Ensemble Classifiers by Diversified Feature Selection and Stochastic Aggregation." Mathematics 12, no. 6 (2024): 834. http://dx.doi.org/10.3390/math12060834.

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Learning-based classifiers are found to be vulnerable to attacks by adversarial samples. Some works suggested that ensemble classifiers tend to be more robust than single classifiers against evasion attacks. However, recent studies have shown that this is not necessarily the case under more realistic settings of black-box attacks. In this paper, we propose a novel ensemble approach to improve the robustness of classifiers against evasion attacks by using diversified feature selection and a stochastic aggregation strategy. Our proposed scheme includes three stages. Firstly, the adversarial feat

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Wilson, Stewart W. "Classifier Fitness Based on Accuracy." Evolutionary Computation 3, no. 2 (1995): 149–75. http://dx.doi.org/10.1162/evco.1995.3.2.149.

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In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X × A → P from inputs and actions to payoff predict

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Ibrahim Bisallah, Hashim, and Umoru Yahaya Ibrahim. "Machine learning base algorithms using truncated singular value decomposition as a novel solution for fake news analysis and detection." KIU journal of science engineering and technology 3, no. 1b (2024): 145–57. http://dx.doi.org/10.59568/kjset-2024-3-1-15.

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Fake news analysis and detection is a method that requires using machine learning classifiers to recognize and detect news content as fake or real. No research work has been carried out on fake news analysis and detection using the machine learning algorithms using Truncated singular value decomposition (TSVD) on the ‘ISOT Fake News Dataset’ but there is a need for a better design that can provide better accuracy. This research aims to develop and use systematic progression in identifying fake news analysis using machine learning algorithms with TSVD. The machine learning algorithms which incl

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Aljero, Mona Khalifa A., and Nazife Dimililer. "A Novel Stacked Ensemble for Hate Speech Recognition." Applied Sciences 11, no. 24 (2021): 11684. http://dx.doi.org/10.3390/app112411684.

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Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines th

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Anagnostopoulos, Theodoros, and Christos Skourlas. "Ensemble majority voting classifier for speech emotion recognition and prediction." Journal of Systems and Information Technology 16, no. 3 (2014): 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.

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Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is construc

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Notomista, Gennaro, and Michael Botsch. "A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking." Journal of Artificial Intelligence and Soft Computing Research 7, no. 4 (2017): 243–55. http://dx.doi.org/10.1515/jaiscr-2017-0017.

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AbstractA classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle-dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that ca

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Sun, Pei Pei, Quan Yin Zhu, Lei Zhou, and Yong Jun Zhang. "Comparative Analysis of Text Categorizer on Science and Technology Intelligence." Applied Mechanics and Materials 530-531 (February 2014): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.502.

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In order to more effectively classify the science and technology intelligence text, the idea that classifying science and technology intelligence text categorization based on different classifiers is proposed. The experiment is done with two thousand Chinese texts based on three different classifiers in this paper. Among these classifiers, the rate of correctly classified instances with NaiveBayes Classifier is 96.95 percent and J48 Classifiers is 97.59. The highest of three classifiers is SMO Classifier and its correct rate is 98.65 percent. According to the analysis of experimental results,

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Mizuta, H., K. Kawachi, H. Yoshida, et al. "Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire." Methods of Information in Medicine 36, no. 04/05 (1997): 349–51. http://dx.doi.org/10.1055/s-0038-1636858.

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Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier.

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Pernkopf, Franz. "Bayesian network classifiers versus selective -NN classifier." Pattern Recognition 38, no. 1 (2005): 1–10. http://dx.doi.org/10.1016/j.patcog.2004.05.012.

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Han, Meng, Shurong Yang, Hongxin Wu, and Jian Ding. "Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group." Mathematics 13, no. 1 (2024): 103. https://doi.org/10.3390/math13010103.

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Ensemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. A heterogeneous ensemble can generate classifiers with better diversity than a homogeneous ensemble and improve the performance of classification results. An Adaptive Heterogeneous Classifier Group (AHCG) algorithm is proposed. The AHCG first proposes the concept of a Heterogeneous Classifier Group (HCG); that is, two groups of different ensemble classifiers are used in the testing and training phases. Secondly, the Adapti

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