Search Results - (( using optimization method algorithm ) OR ( automatic classification tree algorithm ))

A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping by Althuwaynee, Omar F., Pradhan, Biswajeet, Park, Hyuck Jin, Lee, Jung Hyun

“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration by Althuwaynee, Omar F., Pradhan, Biswajeet, Ahmad, Noordin

“…This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. …”
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Towards a better feature subset selection approach by Shiba, Omar A. A.

“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition by Nur Farahaina, Idris, Mohd Arfian, Ismail

“…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island by Mohd Shafri, Helmi Zulhaidi, Ramle, F. S. H.

“…The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. …”
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Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image by Shojanoori, Razieh

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Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

“…The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. …”
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The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification by Mokhairi, Makhtar, Engku Fadzli Hasan, Syed Abdullah, Fatma Susilawati, Mohamad

“…Classification process is part of the important phase in automatic image annotation (AIA). …”
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Data mining of protein sequences with amino acid position-based feature encoding technique by Iqbal, M.J., Faye, I., Md Said, A., Samir, B.B.

“…In this paper, an amino acid position-based feature encoding technique is proposed to represent a protein sequence using a fixed length numeric feature vector. The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 classification accuracy over the Yeast protein sequence dataset. …”
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Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem by Kader, Nur Izzati Ab

“…Hence, this research aims to obtain optimal or near-optimal performance value in the study of diabetic classification using supervised machine learning. There are many algorithms available for classification purpose such as k-Nearest Neighbour, k-Means, Support Vector Machine, Decision Tree, Artificial Neural Network and Linear Discriminant Analysis. …”
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Modelling of the anti-collision algorithm in RFID system: article / Shafinaz Ismail by Ismail, Shafinaz

“…To avoid these collisions, there are several anti-collision algorithms used in the RFID system. The major classifications of the algorithms are Aloha based protocols and tree based protocols. …”
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Modelling of the anti-collision algorithm in RFID system / Shafinaz Ismail by Ismail, Shafinaz

“…To avoid these collisions, there are several anti-collision algorithms used in the RFID system. The major classifications of the algorithms are Aloha based protocols and tree based protocols. …”
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A comparative study in classification techniques for unsupervised record linkage model by Ektefa, Mohammadreza, Sidi, Fatimah, Ibrahim, Hamidah, A. Jabar, Marzanah, Memar, Sara

“…In order to utilize the supervised classification algorithms without consuming a lot of time for labeling data manually, a two step method which selects the training data automatically has been proposed in previous studies. …”
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Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study by Mujtaba, Ghulam, Shuib, Liyana, Raj, Ram Gopal, Rajandram, Retnagowri, Shaikh, Khairunisa

“…Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. …”
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Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks by Muhammad Ashraq, Salahuddin

“…Results show that the overall performance of Neural Network with DOUBLE is 96.3% while actual data set is 68.3%, and the accuracy obtained from Logistic Regression with actual data set is 60.5%. The Decision Tree classification results indicate that the highest performance obtained by Chi-Squared Automatic Interaction Detection(CHAID) and Exhaustive CHAID (EX-CHAID) is merely 42% with DOUBLE. …”
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Analysing the performance of classification algorithms on diseases datasets by Lydia E.L., Sharmil N., Shankar K., Maseleno A.

“…The proposed classification algorithms measure the diseases using the disease datasets which estimates the accurate prediction. …”

A review of chewing detection for automated dietary monitoring by Minhad, Khairun Nisa’, Selamat, Nur Asmiza, Yanxin, Wei, Md Ali, Sawal Hamid, Sobhan Bhuiyan, Mohammad Arif, Kelvin Jian, Aun Ooi, Samdin, Siti Balqis

“…The decision tree approach was more robust and its classification accuracy (75%–93.3%) was higher than those of the Viterbi algorithm-based finite-state grammar approach, which yielded 26%–97% classification accuracy. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

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An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

“…The algorithms include Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), Classification And Regression Tree (CART), Random Forest (RF). …”
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Deep learning metaphor detection with emotion-cognition association by Razali, Md Saifullah, Abdul Halin, Alfian, Chow, Yang-Wai, Mohd Norowi, Noris, Doraisamy, Shyamala

“…These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. …”
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