eugene-kamenev

eugene-kamenev's Stars

• tdunning/t-digest

  A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means

  Language:Java2k226

• oracle/tribuo

  Tribuo - A Java machine learning library

  Language:Java1.3k175

• AtasPlatform/Indicators

  Language:C#5533

• numenta/NAB

  The Numenta Anomaly Benchmark

  Language:Jupyter Notebook1.9k868

• canoalberto/ur-CAIM

  Improved CAIM discretization for unbalanced and balanced data

  Language:Java43

• UniFeat/unifeat

  An open-source tool for performing feature selection process in different areas of research

  Language:Java82

• dtian09/Rough-Set-Feature-Selection-Tools

  Rough set feature selection (RSFS) algorithms implemented in java. RSFS can be used to remove the irrelevant and redundant features from a training set before training machine learning classifiers such as neural networks and Bayesian network to improve the classification performance of the classifiers. The following RSFS algorithms are implemented: genetic algorithm (GA_Reducts.java), QuickReduct (QuickReduct.java), random forward search (Random_RSFS.java), random backward search (Random_RSFS.java), random forward-backward search (Random_RSFS.java), a multi-object genetic local search (Hybrid_NSGAII.java), computation of degree of dependency (Gamma_and_Relative_Dependency.java) and computation of relative dependency (Gamma_and_Relative_Dependency.java).

  Language:Java6

• LJMng/featureSelectionRepository

  Language:Java32

• benjaminpatrickevans/FeatureSelectionWithJava

  Common feature selection algorithms implemented in Java

  Language:Java42

• Yimeng-Zhang/feature-engineering-and-feature-selection

  A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

  Language:Jupyter Notebook1.4k409

• vanbanTruong/FAHT

  Code for reproducing results in FAHT (IJCAI 19)

  Language:Java83

• senge/fuzzy-pattern-trees

  Implementation of several machine learning algorithms to induce fuzzy pattern trees for classification and regression.

  Language:Java52

• mayconbordin/streaminer

  A collection of algorithms for mining data streams

  Language:Java20057

• Suvodeep90/Semi_Supervised_Methods

  Language:Java1

• viadee/discretizer4j

  Discretize all the things!

  Language:Java7

• awojna/Rseslib

  Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.

  Language:Java155

• espertechinc/esper

  Esper Complex Event Processing, Streaming SQL and Event Series Analysis

  Language:Java837257

• jpy-consortium/jpy

  Language:C7117

• pcingola/jFuzzyLogic

  The most complete fuzzy logic library in Java. The de-facto standard for research and industrial applications.

  Language:Java6733

• jpr86/JFuzzy

  Java Fuzzy Logic

  Language:Java2

• nstrek/progressive_search

  Language:Python261

• MathisWellmann/sliding_features-rs

  Modular, chainable sliding windows with various signal processing functions such as normalization, RSI, ROC and other technical indicators.

  Language:Rust399

• lukstei/trading-backtest

  A stock backtesting engine written in Java. And a pairs trading (cointegration) strategy implementation using a bayesian kalman filter model

  Language:Java320139

• haugjo/float

  A modular Python framework for standardized evaluation and benchmarking of online learning models.

  Language:Python91

• cetrulin/groch-moa

  This repository contains the implementation of GroCH, iGNGSVM and RCARF, and scripts to run parameter-tuning for the MOA implementation GroCH in an HPC cluster using SLURM.

  Language:Shell4

• gziffer/kalman-filter

  Kalman Filtering for learning with evolving data streams

  Language:Python2

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

  Language:Jupyter Notebook16.5k4.2k

• baraline/convst

  Implementation of the Random Dilated Shapelet Transform algorithm along with interpretability tools. ReadTheDocs documentation is not up to date with the current version for now.

  Language:Python317

• ByteSumoLtd/TrendCalculus-lua

  The original reference implementation for TrendCalculus. Implemented in Lua.

  Language:Lua81

• lamastex/spark-trend-calculus

  To detect trends in time series using Andrew Morgan's trend calculus algorithms in Apache Spark and Scala from Antoine Amend's initial implementation

  Language:Scala61