majority-voting

[TIFS 2019] Skeleton-based Gait Recognition via Robust Frame-level Matching (RFM)

This project classifies diseases in grape plant using various Machine Learning classification algorithms.

GenPark AI Agent Skill - Self-consistency sampling aggregator, majority voting consensus engine, and semantic clusterer for reasoning verification.

GenPark AI Agent Skill - Self-consistency sampling aggregator, majority voting consensus engine, and semantic clusterer for reasoning verification.

A Credit Card Fraud Detection System using Adaboost and Majority Voting, designed to identify fraudulent credit card transactions by combining the strength of multiple classifiers.

Open-source voting client + server with state-of-the-art systems like Graduated Majority Judgment and Approval Voting for fast, reactive collective decision-making.

I use an agent-based model to explore the impact of imperfect competence and social influence on majority voting. This repository contains the code for an agent-based model and simulations of majority voting, for producing some figures, and for a statistical analysis.

Experimental results capturing the limits on annotation noise under which MV can aggregate labels optimally.

Application for soft voting algorithm demonstration

This repo contains bagging, variance of a model by drawing random bootstrap samples from the training dataset and combining the individually trained classifiers via majority vote; AdaBoost and gradient boosting, which are algorithms based on training weak learners that learn from mistakes.

Multi-Agent Reinforcement Learning framework for collaborative label aggregation on noisy classification datasets. Includes DQN agents, Gym-style environment, evaluation tools, and majority-voting baseline.

An evaluation of prompting techniques (Zero-Shot CoT, Few-Shot, Self-Consistency) on the Mistral-7B model for mathematical reasoning. This project systematically benchmarks 7 distinct methods on the GSM8K dataset.

Worked on a classification analysis (class imbalance) for a business problem. Analysis was done using Anaconda Python.

The project demonstrates the effectiveness of combining AdaBoost and Majority Voting for credit card fraud detection, providing a reliable and accurate solution to combat fraudulent activities in financial transactions.

In this problem statement, a sequence of genetic mutations and clinical evidences, i.e. descriptive texts as recorded by domain experts are used to classify the mutations to conclusive categories, to be used for diagnosis of the patient.

We have developed a Hybrid Model which consists of Random Forest, K-Nearest Neighbors, and Artificial Neural Network Algorithms using the Majority Voting Approach for detecting frauds in Credit Cards effectively and efficiently🙂.

This project focuses on combining the strengths of AdaBoost and majority voting to create a highly efficient fraud detection model. The goal is to provide a reliable method for detecting fraudulent transactions, ensuring the safety of users' financial data.

Run several LLM providers in parallel and consolidate invoice fields by majority vote over a deterministic core. Library plus CLI, offline fake provider, pytest.

Rust crate to manage majority judgment polls

RnD project Collaboration