Search Results - (( re evaluation tree algorithm ) OR ( label classification modified algorithm ))

Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

“…In addition, a contextual text classification experiment is conducted using benchmarked datasets to assess the performance of the modified word vectors in the targeted classification task. …”
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Content-based feature selection for music genre classification by Muda, Noor Azilah, Choo, Yun Huoy, Norashikin, Ahmad

“…We then proposed the Modified AIS-based classification algorithm to solve the music genre classification problem. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling by Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi

“…This paper presents our work to improve the performance of anomaly intrusion detection using Fuzzy-ART based on the K-means algorithm. The K-means is a modified version of the standard K-means by initializing the value K from the value obtained after data mining using Fuzzy-ART and SNC labeling technique. …”
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Boosting and bagging classification for computer science journal by Wibawa, Aji Prasetya, Putri, Nastiti Susetyo Fanany, Al Rasyid, Harits, Nafalski, Andrew, Hashim, Ummi Rabaah

“…In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. …”
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An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease by Haruna, A.A., Muhammad, L.J., Yahaya, B.Z., Garba, E.J., Oye, N.D., Jung, L.T.

“…This research used an im-proved C4.5 data mining algorithm for the diagnosis of CAD. A performance evaluation of the improved algorithm was carried out against the traditional C4.5 Algorithm. …”
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Prediction of COVID-19 patient using supervised machine learning algorithm by M., Buvana, K., Muthumayil

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Customer behavior analysis based on purchasing history and reviews using automated decision-making systems by Allur, Naga Sushma, Deevi, Durga Praveen, Dondapati, Koteswararao, Chetlapalli, Himabindu, Kodadi, Sharadha, Perumal, Thinagaran

“…According to the provided tables, the proposed methods (KNN and ADMS) outperform the other algorithms (ID3, Decision Tree, and Logistic Regression) across a range of customer review counts in terms of accuracy (98.95%), precision (94.62%), recall (99.24%), and F1-score (96.87%) while processing customer reviews more quickly (0.063). …”
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques by Jifroudi, Hamidreza Maskani

“…In the next step, noise and roof errors were removed using KNN filter and a new network was created and re-evaluated based on the shortest distance in the LiDAR point cloud to create an integrated DTM. …”
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Factors with retirement behaviour among retirees and pre-retirees identified with a machine learning method / Muhammad Aizat Zainal Alam by Muhammad Aizat , Zainal Alam

“…This study uses 3,067 responses which are then be coupled with a machine learning methodology (ranging from Naïve Bayesian, Generalised Linear Model, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees) via RapidMiner Studio to expand the understanding of how categories of wealth and expenditures can affect retirement behaviour, given the increasingly important role of machine learning algorithms within the context of behavioural economics where it has been demonstrated to describe patterns and relationships in behavioural data better than standard statistical analysis. …”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model by Dickson Neoh Tze How, Dr.

“…To select the optimal hyperparameters for the Transformer model, the Tree Parzen Estimator(TPE) optimization in combination with the Hyperband pruning algorithm is employed to search for the best combination that yields the lowest Root Mean Squared Error(RMSE)and Mean Absolute Error (MAE) error metrics. …”