Search Results - (( label classification techniques ) OR ( evolution computing techniques ))

Multi-tier classification based on sentiment, type, emotion and purpose for online diabetes community / Wandeep Kaur Ratan Singh by Wandeep Kaur , Ratan Singh

“…Co-training multinomial Naïve Bayes was used where the two base classifiers were used for both label and feature classification. The uniqueness lies in using dimensionality reduction technique of converting numeric vectors to string vectors using Word2Vec that improved F1-Score of 61% compared to only 48%. …”
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Improved classifier chain methods based on heuristic optimization techniques for multi-label classification problem by Oyekunle, Adeleke Abdullahi

“…This thesis is about proposing multi-label classification (MLC) techniques for classification applications. …”
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Nearest neighbour group-based classification by Samsudin, Noor A., Bradley, Andrew P.

“…In this paper, we extend three variants of the nearest neighbour algorithm to develop a number of non-parametric group-based classification techniques. The performances of the proposed techniques are then evaluated on both synthetic and real-world data sets and their performance compared with techniques that label test samples individually. …”
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Multi-label classification for physical activity recognition from various accelerometer sensor positions by Mohamed, Raihani, Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Perumal, Thinagaran, Mustapha, Norwati

“…Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue. …”
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Fuzzy ARTMAP with binary relevance for multi-label classification by Yuan, L.X., Tan, S.C., Goh, P.Y., Lim, C.P., Watada, J.

“…In this paper, we propose a modified supervised adaptive resonance theory neural network, namely Fuzzy ARTMAP (FAM), to undertake multi-label data classification tasks. FAM is integrated with the binary relevance (BR) technique to form BR-FAM. …”
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Fuzzy ARTMAP with binary relevance for multi-label classification by Yuan, L.X., Tan, S.C., Goh, P.Y., Lim, C.P., Watada, J.

“…In this paper, we propose a modified supervised adaptive resonance theory neural network, namely Fuzzy ARTMAP (FAM), to undertake multi-label data classification tasks. FAM is integrated with the binary relevance (BR) technique to form BR-FAM. …”
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Multi-label learning based on positive label correlations using predictive apriori by Al Azaidah, Raed Hasan Saleh

“…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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Stock market classification model using sentiment analysis based on hybrid naive bayes classifiers by A. Jabbar Alkubaisi, Ghaith Abdulsattar

“…Another important factor is the automatic labelling technique which leads to low classification accuracy due to the absence of specific lexicon. …”
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Multi-label classification for physical activity recognition from various accelerometer sensor positions by Mohamed, Raihani, Zainudin, Mohammad Noorazlan Shah, Sulaiman, Md Nasir, Perumal, Thinagaran, Mustapha, Norwati

“…The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately.Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue.The result was compared with several state-of-the-art traditional multi-class classification approaches. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

“…The cluster labelling algorithm was also compared to maximum-likelihood supervised classifier in the production of classification map for MASTER dataset. …”
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Exceeding manual labeling: VADER Lexicon as an accurate alternative to automatic sentiment classification by Vivine Nurcahyawati, ., Zuriani, Mustaffa, Khalaf, Mohammed I.

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An improve unsupervised discretization using optimization algorithms for classification problems by Mohamed, Rozlini, Samsudin, Noor Azah

“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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An improve unsupervised discretization using optimization algorithms for classification problems by Mohamed, Rozlini, Samsudin, Noor Azah

“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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Feature selection techniques for enhancing app user review analysis by Salleh, Amran, Osman, Mohd Hafeez, Hassan, Sa’Adah, Said, Mar Yah

“…This study addresses this challenge by evaluating the impact of different feature selection techniques on the performance of machine learning models in multi-label classification tasks for app user review analysis. …”
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Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling by Mohamed, Raihani

“…Furthermore, there is tendency that multi label classifications used instead of traditional single label classification technique. …”
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Dynamic android malware category classification using semi-supervised deep learning by Mahdavifar, Samaneh, Kadir, Andi Fitriah Abdul, Fatemi, Rasool, Alhadidi, Dima, Ghorbani, Ali A

“…However, it is far to be perfect because it requires a significant amount of malicious and benign code to be identified and labeled beforehand. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique for deep neural networks, namely pseudo-label, which we train using a set of labeled and unlabeled instances. …”
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Fake review annotation model and classification through reviewers' writing style by Shojaee, Somayeh

“…We found that only 7% of the reviews were labeled differently. The other open problem of fake product review classification is the lack of historic knowledge independent feature sets. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…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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Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

“…Classification is a process of grouping or placing data into appropriate categories or classes based on specificattributes or features to predict labels or classes of new data based on patternsobserved from previously trained data. …”
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A comparative study in classification techniques for unsupervised record linkage model by Ektefa, Mohammadreza, Sidi, Fatimah, Ibrahim, Hamidah, A. Jabar, Marzanah, Memar, Sara

“…To find out whether two records are duplicate or not, supervised and unsupervised classification techniques are utilized in different studies. …”
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