Search Results - (( code classification problem algorithm ) OR ( _ classification tree algorithm ))
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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level
Published 2024thesis::doctoral thesis -
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Comparative analysis for topic classification in juz Al-Baqarah
Published 2018“…The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. …”
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3
Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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4
Wearable based-sensor fall detection system using machine learning algorithm
Published 2021“…The fall event behaviour classification classes are sleep, walk, sit, front fall, back fall, side fall, etc. …”
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Proceeding Paper -
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection
Published 2024“…Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. …”
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Phylogenetic tree classification system using machine learning algorithm
Published 2015“…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
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Final Year Project Report / IMRAD -
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Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
Published 2019“…The area is a high potential growing region for different tree species. The main objectives is to develop a forest tree recognition techniques and build a classification strategy for forest tree area segmentation. …”
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Undergraduate Final Project Report -
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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A novel decision tree searcidng algorithm for high speed online traffic classification
“…As such, this section describes and discusses the research methodology employed in both designing and evaluating a novel decision tree searching algorithm for high speed online traffic classification…”
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Research Book Profile -
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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. …”
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Effective k-Means Clustering in Greedy Prepruned Tree-based Classification for Obstructive Sleep Apnea
Published 2022“…Incorporation of prepruned decision trees to kmeans clustering through one to three types of tree-depth controllers and cluster partitioning was done to develop a combined algorithm named as Greedy Pre-pruned Treebased Clustering (GPrTC) algorithm. …”
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Winsorize tree algorithm for handling outliers in classification problem
Published 2016“…This study proposes a modified classification tree algorithm called Winsorize tree based on the distribution of classes in the training dataset. …”
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Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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Classification model for hotspot occurrences using spatial decision tree algorithm
Published 2013“…This study describes the application of data mining technique namely decision tree on forest fires data. We improved the ID3 decision tree algorithm such that it can be utilized on spatial data in order to develop a classification model for hotspots occurrence. …”
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Case Slicing Technique for Feature Selection
Published 2004“…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan Abdul Manan
Published 2023“…To achieve these objectives, the methods employed involve data preprocessing and implementing the Decision Tree (DT) algorithm for classification. The classification model is trained and tested using various split ratios, and the optimal depth for the DT is determined through parameter tuning to achieve the best accuracy. …”
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A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island
Published 2009“…The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
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Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image
Published 2014“…This algorithm provides a decision tree output to represent the knowledge model, enabled a faster classification of intra-urban classes, and disabled the subjectivities which are related to the interaction of the analyst. …”
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Conference or Workshop Item -
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An extended ID3 decision tree algorithm for spatial data
Published 2011“…One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. …”
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Conference or Workshop Item
