Search Results - ((pattern classification) OR (((based classification) OR (_ classification)))) problems

Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

“…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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A survey of fuzzy min max neural networks for pattern classification: variants and applications by Al Sayaydeh, Osama Nayel, Mohammed, Mohammed Falah, Lim, Chee Peng

“…This division facilitates understanding of the improvements on the original FMM model, as well as enables identification of the limitations that still exist in various FMM-based models. We also summarize the use of FMM and its variants in solving different benchmark and real-world pattern classification problems. …”
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Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach by Moheb Pour, Majid Reza

“…Pattern recognition/classification has received a considerable attention in engineering fields. …”
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Text classification using modified multi class association rule by Kamaruddin, Siti Sakira, Yusof, Yuhanis, Husni, Husniza, Al Refai, Mohammad Hayel

“…This paper presents text classification using a modified Multi Class Association Rule Method.The method is based on Associative Classification which combines classification with association rule discovery. …”
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An enhanced generalized adaptive resonance theory neural network and its application to medical pattern classification by Yap K.S., Lim C.P., Mohamad-Saleh J.

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A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification by Quteishat, A., Lim, C.P., Tan, K.S.

“…In this paper, a two-stage pattern classification and rule extraction system is proposed. …”
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A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification by Alhroob, Essam, Mohammed, Mohammed Falah, Lim, Chee Peng, Tao, Hai

“…FMM is considered one of the most useful neural networks for pattern classification. This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement. …”
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A derivative-free optimization method for solving classification problem by Shabanzadeh, Parvaneh, Abu Hassan, Malik, Leong, Wah June

“…There is a training set for each class. Those problems occur in a wide range of human activity. One of the most promising ways to data classification is based on methods of mathematical optimization. …”
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Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression by Yap , Keem Siah

“…This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. …”
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Thresholding and Fuzzy Rule-Based Classification Approaches in Handling Mangrove Forest Mixed Pixel Problems Associated with in QuickBird Remote Sensing Image Analysis by Mohd, Othman *, Nanna , Suryanna, Sahib@Sahibuddin, Shahrin, Abdollah, Mohd Faizal, Selamat, Siti Rahayu

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Grid base classifier in comparison to nonparametric methods in multiclass classification by Moheb Pour, Majid Reza, Jantan, Adznan, Saripan, M. Iqbal

“…The method was developed based on the grid structure which was done to create a powerful method for classification. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…Data level based methods are meant to solve the imbalanced classification problem based on the idea of making both classes equal in number. …”
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A new feature-based wavelet completed local ternary pattern (FEAT-WCLTP) for texture and medical image classification by Shamaileh, Abeer Moh'd Salem

“…The proposed Feat-WCLTP not only overcomes the dimensionality problem but also considerably improves the classification accuracy. …”
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Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

“…Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance rather than the combination of both problems. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

“…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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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Rough Set Discretize Classification of Intrusion Detection System by Noor Suhana, Sulaiman, Rohani, Abu Bakar

“…Many pattern classification tasks confront with the problem that may have a very high dimensional feature space like in Intrusion Detection System (IDS) data. …”
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Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification by Navan, Farhad Pourpanah

“…The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks. …”
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Flexible enhanced fuzzy min–max neural network model for pattern classification problems by Al-Hroob, Essam Muslem Harb

“…The results demonstrate the efficiency of FEFMM in handling pattern classification problems and providing a superior performance of classification accuracy as compared to the other network structures from the same variants such as EFMM, FMM variants and also non-FMM related models. …”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

“…On top of that, random forest ensemble classifier model has reported successive perform in most classification and pattern recognition problems. The expanding of randomness layer in the traditional decision tree is able to increase the diversity of classification accuracy. …”
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