Tweets preprocessor

Weka package for missing values imputation and injection using various techniques.

Japanese text normalizer for mecab-neologd

GLSL Preprocessing with the C Preprocessor in Java - based on JCPP

Weka package for the snowball stemmers (http://snowball.tartarus.org/).

CAIRAD class implements the CAIRAD techique for detecting noisy values in a dataset for Weka

Implements the DMI imputation algorithm for imputing missing values in a dataset from Rahman, M. G., and Islam, M. Z. (2013): Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques

Weka package containing various natural language processing components.

Weka package for the PTStemmer (https://code.google.com/p/ptstemmer/).

A simple framework to do frame preprocessing with Android Camera2 API.

A collection of utlities for dealing with Arma files in Java

Weka package using the mXparser library.

Language processing interface: some tools to process different natural languages

Weka package with filters that allow modifying attribute/instance weights.

A parser written for the BabyCobol language, using the ANTLR framework. This repository is part of my bachelor thesis.

LFD is a data-driven discretization technique that does not require any user input. LFD uses low frequency values as cut points and thus reduces the information loss due to discretization. It uses all other categorical attributes and any numerical attribute that has already been categorized.

degree project at bachelor level

Weka package that allows listening in on data as it passes through filter pipelines.

kDMI employs two levels of horizontal partitioning (based on a decision tree and k-NN algorithm) of a data set, in order to find the records that are very similar to the one with missing value/s. Additionally, it uses a novel approach to automatically find the value of k for each record.

SiMI imputes numerical and categorical missing values by making an educated guess based on records that are similar to the record having a missing value. Using the similarity and correlations, missing values are then imputed. To achieve a higher quality of imputation some segments are merged together using a novel approach.