Search Results - (( based optimization svm algorithm ) OR ( data classification problems algorithm ))

A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

“…This paper describes a new application of the Bees Algorithm to the optimization of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. …”
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Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

“…Classification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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Rao-SVM machine learning algorithm for intrusion detection system by Abd, Shamis N., Alsajri, Mohammad, Ibraheem, Hind Raad

“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
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Mutable composite firefly algorithm for gene selection in microarray based cancer classification by Fajila, Mohamed Nisper Fathima

“…A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

“…One of the outstanding classifications methods in data mining is support vector machine classification (SVM). …”
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Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

“…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol by Md Fisol, Nur Atiqah Izzati

“…Children abandoned in vehicles is a critical issue that has led to numerous fatal injuries worldwide. To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data by Mohammad Aman Ullah

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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Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set by Yap, Chau Tean

“…Six machine learning algorithms were applied and compared based on the classification evaluation methods. …”
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SVM for network anomaly detection using ACO feature subset by Mehmood, T., Rais, H.B.M.

“…But irrelevant and redundant features are the obstacle for classification algorithm to build an efficient detection model. …”
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Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection by Alsmadi, Issa Mohammad Ibrahim

“…In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature.…”
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Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification by Al-Sharhan, Salah, Bimba, Andrew

“…The classification was done using a Support Vector Machine (SVM). …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Dasril, Yosza, swanto, Iswanto

“…However, the big problem with prediction is data imbalance and low performance classification algorithms. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi b, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…However, the big problem with prediction is data imbalance and low performance classification algorithms. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…However, the big problem with prediction is data imbalance and low performance classification algorithms. …”
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