Search Results - (( associative classification using algorithm ) OR ( java implication bees algorithm ))

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function by Nur Farahaina, Idris, Mohd Arfian, Ismail, Mohd Saberi, Mohamad, Shahreen, Kasim, Zalmiyah, Zakaria, Sutikno, Tole

“…The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. …”
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Using fuzzy association rule mining in cancer classification by Mahmoodian, Sayed Hamid, Marhaban, Mohammad Hamiruce, Abdul Rahim, Raha, Rosli, Rozita, Saripan, M. Iqbal

“…A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. …”
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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review by Nur Farahaina, Idris, Mohd Arfian, Ismail

“…Decision tree is an important method in data mining to solve the classification problems. There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. …”
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Ant colony algorithm for web page classification by Moayed, Majid Javid, Sabery, A. Hamid, Khanteymoory, Alireza

“…Ant Miner II is the used algorithm. It also propose a simple text preprocessing technique to reduce the large numbers of attributes associated with web content mining, without dealing linguistic complications. …”
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Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

“…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

“…The comparison showed that, the accuracy of the unsupervised classification map with value of 88.4% that was generated by using the cluster labelling algorithm was slightly more than the maximum-likelihood supervised classification map with value of 87.5%. …”
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Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…Eight benchmark datasets from UCI were used in the experiments to validate the performance of the proposed algorithms. …”
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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. …”
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Breast cancer detection by using associative classifier with rule refinement method based on relevance feedback by Abubacker, Nirase Fathima, Azman, Azreen, Doraisamy, Shyamala, Azmi Murad, Masrah Azrifah

“…Several researchers have proposed the use of associative classifier that generates strong associations between features and reveals hidden relationship that can be missed by other classification algorithms. …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

“…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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Evaluation and optimization of frequent association rule based classification by Izwan Nizal Mohd Shaharanee, Jastini Jamil

“…In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. …”
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Evaluation and optimization of frequent, closed and maximal association rule based classification by Mohd Shaharanee, Izwan Nizal, Hadzic, Fedja

“…Empirical results also show the downfall of using the confidence measure at the start to generate association rules, as typically done within the association rule framework. …”
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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An enhanced feature selection technique for classification of group based holy Quran verses by Abdullahi Oyekunle, Adeleke

“…However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. …”
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Classification of Diabetes Mellitus using Ensemble Algorithms by Noor, N.A.B.S., Elamvazuthi, I., Yahya, N.

“…The objective of this study is to perform DM classification using various machine learning algorithms. …”
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A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization by Chern-Tong, H., Aziz, I.A.

“…The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. …”
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A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization by Chern-Tong, H., Aziz, I.A.

“…The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. …”
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Irrelevant feature and rule removal for structural associative classification by Mohd Shaharanee, Izwan Nizal, Jamil, Jastini

“…In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms,in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem.Practical applications of association rule mining often suffer from overwhelming number of rules that are generated, many of which are not interesting or not useful for the application in question.Removing rules comprised of irrelevant features can significantly improve the overall performance.In this paper, we explore and compare the use of a feature selection measure to filter out unnecessary and irrelevant features/attributes prior to association rules generation.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data items.Empirical results confirm that by utilizing feature subset selection prior to association rule generation, a large number of rules with irrelevant features can be eliminated.More importantly, the results reveal that removing rules that hold irrelevant features improve the accuracy rate and capability to retain the rule coverage rate of structural associative association.…”
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Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment by Nanna Suryana, Herman

“…Region growing segmentation and radial basis function algorithms are considered a powerful tool to minimize the mixed pixel classification error.…”
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