Evolutionary Pac-Man bots using Grammatical Evolution and Multi-objective Optimization. Cool GUI included (Undergraduate Thesis)

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

java math expression parser is faster than JEP

Higson - Motor Insurance Demo App. This is a sample application to demonstrate capabilities of Higson.io library (Java Business Rules Engine (BRE)/Java Pricing Engine). The application demonstrates responsive quotations for Car/Motor Insurance based on decision tables and Rhino functions (for math calculations).

💉🤒 The project was built using maven project utilized WEKA in building the decision tree model and JavaFX in building GUI 💉🤒

Source code related to the ILP 2019 paper 'LazyBum: Decision tree learning using lazy propositionalization'

Distributed decision-making system with Jade and Weka

A MapReduce Version of Random Forest.

Source code for the paper "Interpretable models from distributed data via merging of decision trees" (Artur Andrzejak, Felix Langner, Silvestre Zabala, CIDM 2013).

A potential replica of the famous Akinator

Class implementing decision forest algorithm Forest PA, using bootstrap samples and penalized attributes. Uses and depends on SimpleCart.

Kokomo is a competitor robot built for Robocode that uses regression tree based machine learning (called "Dynamic Segmentation" on the Robocode wiki) for its targeting strategy

We present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously…

Fletcher, S., & Islam, M. Z. (2017). Differentially private random decision forests using smooth sensitivity. Expert Systems with Applications, 78, 16-31.

Object/animal guessing game (a.k.a. 20 Questions game), which learns on your answers, implemented using Decision Tree Learning in Java

Lab solutions for Artificial Intelligence ("Umjetna inteligencija") course at FER 2019/20 led by izv. prof. dr. sc. Jan Šnajder and doc. dr. sc. Marko Čupić

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

Decision Tree to classify ground water samples to potable and not potable, also applied on wine dataset and horse datasets to classify them into different classes

Hadoop Lab Programs

Implementation of the decision forest algorithm SysFor, a forest of high accuracy decision trees.