A fast and easy to use decision tree learner in java

simple rules engine

Devs是一款轻量级的规则引擎

Package implements decision tree and isolation forest

Rapid and precise prediction of Mycobacterium tuberculosis complex families

An android application for tackling mental health related issues

A MapReduce Version of Random Forest.

Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained kn…

Implement Genetic Algorithm and Decision Tree

Alpha–Beta Pruning Implementation for Optimal Decision-Making

Genetic AI for Poker Squares

This repository contains all the assignments that I completed for AI. It includes ILS, Tabu Search, Genetic Algorithms, Genetic Programming, Ant Colony Optimization and Decision Trees

A parser for scikit-learn exported text models to execute in the Java runtime.

🎁 Three classic datasets are provided for learning and practice, aiming to understand and be familiar with ML algorithms and data mining in the field of feature extraction and data classification through different methods in Weka.

This was done as a project in machine learning course in Birzeit University

Implementing a decision tree data type for text classification.

Decisions4J is a Java library for creating decision trees. It provides a simple API for making decisions based on custom rules and conditions. Ideal for lightweight, simple decision-making applications.

The aim of this project was take a brief journey to the world of Artificial Intelligence by exploring how a search algorithm searches a decision tree.

This is a java project focused on Data Mining which contains Normalizing Data and for the Classification Algorithms, I include the Association Rule and Decision Tree (ID3).

Implementation of several classification algorithms in Java. In addition to algorithms, it was necessary to implement tools for reading data, validation and evaluation metrices.