Search Results - (( feature classification tree algorithm ) OR ( evolution optimization method algorithm ))

Refine Results
  1. 1

    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals by Too, Jing Wei

    Published 2020
    “…The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  2. 2

    EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization by Too, Jing Wei, Tee, Wei Hown, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

    Published 2019
    “…In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. …”
    Get full text
    Get full text
    Get full text
    Article
  3. 3

    Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

    Published 2015
    “…The aim is to detennine the suitable features for the phylogenetic tree image classification systeIlJ. …”
    Get full text
    Get full text
    Get full text
    Final Year Project Report / IMRAD
  4. 4

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    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). …”
    Get full text
    Get full text
    Thesis
  5. 5

    VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS by KAMARUDDIN, ERNA SHAFFIQA

    Published 2018
    “…The study is conducted to analyse the performance of histograms of oriented gradient(HOG) between Linear SVM algorithm and Aggregated Channel Features (ACF) algorithm which is using Decision Trees. …”
    Get full text
    Get full text
    Final Year Project
  6. 6

    An extended ID3 decision tree algorithm for spatial data by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    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. …”
    Get full text
    Get full text
    Conference or Workshop Item
  7. 7

    Classification model for hotspot occurrences using spatial decision tree algorithm by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    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. …”
    Get full text
    Get full text
    Get full text
    Article
  8. 8

    Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree by Arowolo, Micheal Olaolu, Adebiyi, Marion Olubunmi, Adebiyi, Ayodele Ariyo

    Published 2021
    “…The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. …”
    Get full text
    Get full text
    Get full text
    Article
  9. 9

    Improved random forest for feature selection in writer identification by Sukor, Nooraziera Akmal

    Published 2015
    “…An algorithm and framework of Improved Random Forest (IRF) tree was applied for feature selection process. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  10. 10

    Behavioural features for mushroom classification by Ismail, Shuhaida, Zainal, Amy Rosshaida, Mustapha, Aida

    Published 2018
    “…The Principal Component Analysis (PCA) algorithm is used for selecting the best features for the classification experiment using Decision Tree (DT) algorithm. …”
    Get full text
    Get full text
    Article
  11. 11

    Intersection Features For Android Botnet Classification by Ismail, Najiahtul Syafiqah, Yusof, Robiah, Saad, Halizah, Abdollah, Mohd Faizal, Yusof, Robiah

    Published 2019
    “…This paper proposed an enhancement approach for Android botnet classification based on features selection and classification algorithms. …”
    Get full text
    Get full text
    Get full text
    Article
  12. 12

    Formulation of invariants for discrete orthogonal moments and image classification / Pee Chih Yang by Pee, Chih Yang

    Published 2013
    “…Due to the complexity of hypergeometric functions, existing invariant algorithms are slow. In addition, some of the features have poor classification performance and are highly sensitive to the noise. …”
    Get full text
    Get full text
    Thesis
  13. 13

    Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

    Published 2018
    “…First, features selection algorithms (genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS)) were used to select the most significant frequencies. …”
    Get full text
    Get full text
    Thesis
  14. 14
  15. 15

    Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi, Mansor, Shattri, Ahmad, Noordin

    Published 2014
    “…The images were used to explore the combined performance of a data mining (DM) algorithm and object-based image analysis (OBIA). A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. …”
    Get full text
    Get full text
    Article
  16. 16

    Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi

    Published 2016
    “…Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Study Of EMG Feature Selection For Hand Motions Classification by Abdullah, Abdul Rahim, Mohd Saad, Norhashimah, Too, Jing Wei

    Published 2019
    “…Thus, this paper employs two recent feature selection methods namely competitive binary gray wolf optimizer (CBGWO) and modified binary tree growth algorithm (MBTGA) to evaluate the most informative EMG feature subset for efficient classification. …”
    Get full text
    Get full text
    Get full text
    Article
  18. 18

    Analysis of Acoustic Emission Signal for Prediction of Corrosion on Carbon Steel Pipelines by Kafi, N.A., May, Z.B.

    Published 2021
    “…Six features were selected to be used as input into the two algorithms. …”
    Get full text
    Get full text
    Conference or Workshop Item
  19. 19

    A study on classification learning algorithms to predict crime status. by Shojaee, Somayeh, Mustapha, Aida, Sidi, Fatimah, A. Jabar, Marzanah

    Published 2013
    “…In this paper, we conducted an experiment to obtain better supervised classification learning algorithms to predict crime status by using two different feature selection methods tested on real dataset. …”
    Get full text
    Get full text
    Article
  20. 20

    Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

    Published 2021
    “…Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article