Search Results - (( parameter optimization based algorithm ) OR ( label classification problems algorithm ))
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1
Hyper-heuristic framework for sequential semi-supervised classification based on core clustering
Published 2020“…Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. …”
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2
Multi-label learning based on positive label correlations using predictive apriori
Published 2019“…Multi-label Learning (MLL) is a general task in data mining that consists of three main tasks: classification, label ranking, and multi-label ranking. …”
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3
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…In addition, the labelling is time consuming and done manually. To solve the problems mentioned, integration of unsupervised clustering algorithm and the supervised classifier is proposed. …”
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4
An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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5
An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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6
Multi label ranking based on positive pairwise correlations among labels
Published 2020“…Multi-Label Classification (MLC) is a general type of classification that has attracted many researchers in the last few years. …”
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7
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…The focus of this thesis is on solvingclustering and classification problems. Specifically, we will focus on new optimization methods for solving clustering and classification problems. …”
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8
Nearest neighbour group-based classification
Published 2010“…This can be seen as a simplification of the well studied, but computationally complex, non-sequential compound classifica�tion problem. In this paper, we extend three variants of the nearest neighbour algorithm to develop a number of non-parametric group-based classification techniques. …”
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9
Context enrichment framework for sentiment analysis in handling word ambiguity resolution
Published 2024“…Eventually, the assessed models of WAR and NAM, along with the evaluated word polarity extraction from dictionary lexicons, are integrated into the proposed CEF. Machine learning algorithms are deployed to perform sentiment classification. …”
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10
Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. …”
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11
Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling
Published 2018“…In accordance to the mentioned problem, Label Combination (LC) of multi label classification is introduced because of its ability to transform the multi label problem into 2ᶫ multi-class problem and exploit the correlation between the class labels. …”
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12
Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…Furthermore, HSS-ELM maintains almost all the advantages of the traditional ELM such as the significant training efficiency and straightforward implementation for multiclass classification problems. The proposed algorithm is tested on publicly available datasets. …”
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13
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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14
Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout
Published 2025“…Seven machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DTT), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Deep Neural Network (DNN)—were used for multi-label classification of the complications. The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
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15
Visual codebook analysis in image understanding / Hoo Wai Lam
Published 2015“…As a resultant of that, visual codebook will learn wrong information, and thus affects the image classification performance. To deal with this problem, soft class labels are proposed in a way that both image level and patch level information are utilized. …”
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16
A direct ensemble classifier for learning imbalanced multiclass data
Published 2013“…Many real-world multiclass classification problems can be represented into a setting where non-crisp label need to be observed. …”
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17
An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images
Published 2012“…The defect classification algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. …”
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18
Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling
Published 2023Conference Paper -
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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20
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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