14 skills found
vecxoz / VecstackPython package for stacking (machine learning technique)
ikki407 / StackingStacked Generalization (Ensemble Learning)
fukatani / Stacked GeneralizationLibrary for machine learning stacking generalization.
QiningDeng / An Interpretable Machine Learning Framework For Slope Stability AnalysisThis is an ensemble learning model that utilizes the stacking ensemble strategy. Using the 3D slope stability coefficient regression prediction task as an example, this ensemble regression model demonstrates good prediction accuracy and generalization capabilities. We welcome code contributions to improve this model and validate it on more tasks.
dustinstansbury / Stacked GeneralizationPython implementation of stacked generalization classifier. Plays nice with sklearn.
caioaao / WolpertA stacked generalization framework. Built on top of scikit learn
hiarindam / Document Image Classification TL SGDocument Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
ndemir / StackingTemplate for Stacking (Stacked Generalization) Ensemble Method
sambalshikhar / Document Image Classification With Intra Domain Transfer Learning And Stacked Generalization Of DeepRVL-CDIP could be looked at as the equivalent of ImageNet for the document image community. It’s certainly the largest we’ve seen in the literature. There are 400,000 total document images in the dataset. The dataset contains much noise and variance in composition of each document class. Uncompressed, the dataset size is ~100GB, and comprises 16 classes of document types, with 25,000 samples per classes. Example classes include email, resume, and invoice. Achieved an Accuracy of over 93% which beat the benchmark score of 92% based on https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip
CodeByHarri / Stacking Ensemble Machine LearningStacking Machine Learning Models. Tunning; feature engineering, scaling, models combinations and parameters.
priscilla100 / Ensemble IDSThe exponential increase in the number of connected "things" and the proliferation in the usage of Internet of Things (IoT) devices has raised numerous challenges in terms of security, privacy, and interoperability. IoT devices are resource constrained in terms of computational power, onboard memory, network bandwidth, and energy availability which limits the implementation of cryptographic solutions. The heterogeneous nature of IoT devices makes them avenue for an attacker to exploit threats like spoofing, routing, MITM, and DoS attacks. With the current sophistication of threats IoT devices are subjected to, an Intrusion Detection System (IDS) is the preferred solution for IoT devices. An IDS continuously monitors incoming traffic, and analyzes it to detect possible signs of cyber threats. This research proposes a novel intelligent ensemble-based IDS that will reside in the IoT gateway. The uniqueness of our approach is to use an ensemble learning technique which combines multiple machine learning techniques in order to the improve the predictive performance and detection accuracy. Ensemble learning have been studied to increase the detection rate while obtaining better generalization performance due to the combination of several machine learning model also known as base learners. Three popularly known ensemble models (i.e. boosting, stacking, and voting) are used in evaluating the performance of our proposed IDS using three machine learning techniques: Decision Tree, Naive Bayes (NB), and k-Nearest Neighbor (KNN). Lastly, the proposed approach will be evaluated on two publicly available dataset; Intrusion Detection Evaluation Dataset (CIC-IDS2017) and N-BaIoT.
ayushilodha / Analysis Prediction Of Crime HotspotsFinal year engineering group project developed using Stacked Generalization Machine Learning Approach
sergeant-wizard / Stacked Generalizationobject-oriented stacked generalization in python
LBMercado / Stacked Generalization Ensemble Learning For Air Pollutant Concentration PredictionWe aim to create an regressor ensemble model composed of models which are analyzed separately and then combined together using the stacked generalization algorithm from the sklearn library.
