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This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.
Use machine learning models to detect lies based solely on acoustic speech information
Some example projects that was made using Tensorflow (mostly). This repository contains the projects that I've experimented-tried when I was new in Deep Learning.
This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.
Pixel based classification of satellite imagery - feature generation using Orfeo Toolbox, feature selection using Learning Vector Quantization, CLassification using Decision Tree, Neural Networks, Random Forests, KNN and Naive Bayes Classifier
This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project
This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset available on kaggle.
Detection of Object-Based Forgery in Advanced Video
📄 Official implementation regarding the paper "Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification".
In this project, the success results obtained from SVM, KNN and Decision Tree Classifier algorithms using the data we have created and the results obtained from the ensemble learning methods Random Forest Classifier, AdaBoost and Voting were compared.
Multiple Model Ensembling
Used ensemble methods such as boosting, voting, Bagging
Building the best machine learning model to detect phishing websites.
It is the nlp task to classify empathetic dialogues datasets using RoBERTa, ERNIE-2.0 and XLNet with different preprocessing method. You can get some detailed introduction and experimental results in the link below.
How to Train an Ensemble of Convolutional Neural Networks for Image Classification (Article on Medium)
Two ensemble models made from ensembles of LightGBM and CNN for a multiclass classification problem.
Machine Learning codes
This notebook investigates whether multiple CNN models can achieve higher classification accuracy than any individual model.
The project is about outlier detection with different methods same as FastVOA, Kmeans, DBScan or LOF, conducted on KDD dataset.
Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
Udacity capstone project | Credit card fraud prediction | Supervised Learning | Ensemble model | Data Sampling
COVID-19 Diagnosis with CNN to extract features and Ensemble ML models to predict.
Deep Learning Face Detection and Verification
The process of combining different machine Algorithms so as to decrease variance,bias in Oder to improve prediction
👨💻 Developed AI Models - Ensemble of Random Forest & SVM and XGBoost classifiers to classify five types of Arrhythmic Heartbeats from ECG signals - published by IEEE.
Εxercises for Machine Learning course in Faculty of Informatics of Aristotle's University of Thessaloniki
Hierarchy decomposition pipeline is a supervised machine learning tool that constructs random forest ensembles from data sets with hierarchical class.
Ensemble Classifier
Face Recognition by Eigenface method with the trained Feed Forward Neural Network and other classifiers applied to biometric attendance system functional on static-images.
Spam Detection – Cluster SMS messages to “Spam” and “Ham” (Kaggle Challenge)
Intro to Machine Learning Final Project
Dynamic Ensemble Diversification