Search Results - (( binary classification modeling algorithm ) OR ( data classifications _ algorithm ))

An improved pixel-based and region-based approach for urban growth classification algorithms / Nur Laila Ab Ghani by Ab Ghani, Nur Laila

“…The urban growth images obtained are analysed to improve existing classification algorithms. The improved algorithm is constructed by adding new parameter and classification rule to existing algorithm. …”
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An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

“…The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and ensemble concept. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO by Sharifah Sakinah, Syed Ahmad

“…The algorithm’s performance is illustrated by the corresponding table of the classification rate. …”
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An enhanced soft set data reduction using decision partition order technique by Mohammed, Mohammed Adam Taheir

“…The second contribution is enhancing the probability of search domain of Markov chain model. Furthermore, this research proposes an efficient Soft-Set Reduction accuracy based on Binary Particle Swarm optimized by Biogeography-Based Optimizer (SSR-BPSO-BBO) algorithm that can generate accurate decision for optimal and sub-optimal results. …”
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New Algorithm of Location Model based on Robust Estimators and Smoothing Approach by Hamid, Hashibah

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Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification by Riko, Febrian, Anne Mudya, Yolanda

“…The Support Vector Machine (SVM) algorithm is employed to evaluate classification performance, focusing on binary classification into "high" and "low" categories in this study. …”
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Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

“…The hybrid method was exemplified in a binary classification between digits ‘4’ and ‘9’ from a multiple features dataset. …”
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A novel performance metric for building an optimized classifier by Hossin, Mohammad, Sulaiman, Md. Nasir, Mustapha, Aida, Mustapha, Norwati

“…Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the accuracy metric alone for all binary data sets. …”
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A Novel Performance Metric for Building an Optimized Classifier by Hossin, M., Sulaiman, M.N, Mustapha, A., Mustapha , N.

“…Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the accuracy metric alone for all binary data sets. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…Based on the above components and circumstances, many studies have been performed on data clustering problems. Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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Airline flight delay prediction using Naïve Bayes algorithm / Ahmad Adib Baihaqi Shukri by Shukri, Ahmad Adib Baihaqi

“…Three algorithms (Gaussian Naïve Bayes, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)) were trained and tested to complete the binary classification of flight delays. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…The proposed method managed data and model complexity effectively while maintaining high computational efficiency. …”
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Machine learning approach of predicting Airline flight delay using Naïve Bayes Algorithm / Ahmad Adib Baihaqi Shukri ... [et al.] by Shukri, Ahmad Adib Baihaqi, Mohamed Yusoff, Syarifah Adilah, Warris, Saiful Nizam, Abu Bakar, Mohd Saifulnizam, Kadar, Rozita

“…The KNN and SVM algorithms were alsotrained and tested to complete the binary classification of flight delays for benchmarking purposes. …”
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Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq by Uzair , Ishtiaq

“…A Convolutional Neural Network (CNN) model was created from scratch for this study. Combining Local Binary Patterns (LBP) based texture features and deep learning features resulted in the creation of the fused features vector which was then optimized using Binary Dragonfly Algorithm (BDA) and Sine Cosine Algorithm (SCA). …”
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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…In order to improve recognition of high interclass similarity activities, One-Versus- All (OVA) binarization strategy is introduced by transforming original multi-class classification problems into a series of two-class classification problems. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…Keyword: Random Forest, Bayesian Inference, Classification, Regression, Missing Data.…”
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Named entity recognition using a new fuzzy support vector machine. by Mansouri, Alireza, Affendy, Lilly Suriani, Mamat, Ali

“…Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. …”
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Deep learning for EEG data analysis by Cheah, Kit Hwa

“…In this project, deep neural network architectures have been constructed to perform binary classification on an EEG dataset that was shown by traditional EEG feature extraction methods to have no significant difference between its two data pools (resting EEG recorded before and recorded after listening to music). …”
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