Projects supporting the arff data format.
Showing Items 1-20 of 25 on page 1 of 2: 1 2 Next

Logo Hub Miner 1.0

by nenadtomasev - November 12, 2014, 19:41:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 330 views, 50 downloads, 1 subscription

About: Hubness-aware Machine Learning for High-dimensional Data

Changes:

Initial Announcement on mloss.org.


Logo python weka wrapper 0.1.13

by fracpete - November 1, 2014, 09:59:54 CET [ Project Homepage BibTeX Download ] 6197 views, 1307 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • added "get_class" method to "weka.core.utils" which returns the Python class object associated with the classname in dot-notation
  • "from_commandline" method in "weka.core.utils" now takes an optional "classname" argument, which is the classname (in dot-notation) of the wrapper class to return - instead of the generic "OptionHandler"
  • added "Kernel" and "KernelClassifier" convenience classes to better handle kernel based classifiers

Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2220 views, 462 downloads, 1 subscription

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

Changes:

improved testing, improved documentation, windows compatibility, more algorithms


Logo JMLR JKernelMachines 2.4

by dpicard - July 24, 2014, 13:51:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13197 views, 3288 downloads, 2 subscriptions

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About: machine learning library in java for easy development of new kernels

Changes:

Version 2.4

  • Added a simple GUI to rapidly test some algorithms
  • New Active Learning package
  • New algorithms (LLSVM, KMeans)
  • New Kernels (Polynomials, component wise)
  • Many bugfixes and improvements to existing algorithms
  • Many optimization

The number of changes in this version is massive, test it! Don't forget to report any regression.


Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 24928 views, 7301 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


Logo ADAMS 0.4.6

by fracpete - June 23, 2014, 06:35:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7791 views, 1659 downloads, 1 subscription

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

Changes:
  • 15 new actors
  • new MEKA addons module (multi-label extension to WEKA)
  • overhauled plugin framework for ImageViewer and SpreadSheet file viewer
  • fixed twitter integration (replay of archives was broken)

Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11781 views, 4643 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


Logo SAMOA 0.0.1

by gdfm - April 2, 2014, 17:09:08 CET [ Project Homepage BibTeX Download ] 756 views, 220 downloads, 1 subscription

About: SAMOA is a platform for mining big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms.

Changes:

Initial Announcement on mloss.org.


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 24352 views, 4259 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo Chordalysis 1.0

by fpetitjean - March 24, 2014, 01:22:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1002 views, 235 downloads, 1 subscription

About: Log-linear analysis for high-dimensional data

Changes:

Initial Announcement on mloss.org.


Logo ELKI 0.6.0

by erich - January 10, 2014, 18:32:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10830 views, 1940 downloads, 3 subscriptions

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

Changes:

Additions and Improvements from ELKI 0.5.5:

Algorithms

Clustering:

  • Hierarchical Clustering - the slower naive variants were added, and the code was refactored
  • Partition extraction from hierarchical clusterings - different linkage strategies (e.g. Ward)
  • Canopy pre-Clustering
  • Naive Mean-Shift Clustering
  • Affinity propagation clustering (both with distances and similarities / kernel functions)
  • K-means variations: Best-of-multiple-runs, bisecting k-means
  • New k-means initialization: farthest points, sample initialization
  • Cheng and Church Biclustering
  • P3C Subspace Clustering
  • One-dimensional clustering algorithm based on kernel density estimation

Outlier detection

  • COP - correlation outlier probabilities
  • LDF - a kernel density based LOF variant
  • Simplified LOF - a simpler version of LOF (not using reachability distance)
  • Simple Kernel Density LOF - a simple LOF using kernel density (more consistent than LDF)
  • Simple outlier ensemble algorithm
  • PINN - projection indexed nearest neighbors, via projected indexes.
  • ODIN - kNN graph based outlier detection
  • DWOF - Dynamic-Window Outlier Factor (contributed by Omar Yousry)
  • ABOD refactored, into ABOD, FastABOD and LBABOD

Distances

  • Geodetic distances now support different world models (WGS84 etc.) and are subtantially faster.
  • Levenshtein distances for processing strings, e.g. for analyzing phonemes (contributed code, see "Word segmentation through cross-lingual word-to-phoneme alignment", SLT2013, Stahlberg et al.)
  • Bray-Curtis, Clark, Kulczynski1 and Lorentzian distances with R-tree indexing support
  • Histogram matching distances
  • Probabilistic divergence distances (Jeffrey, Jensen-Shannon, Chi2, Kullback-Leibler)
  • Kulczynski2 similarity
  • Kernel similarity code has been refactored, and additional kernel functions have been added

Database Layer and Data Types

Projection layer * Parser for simple textual data (for use with Levenshtein distance) Various random projection families (including Feature Bagging, Achlioptas, and p-stable) Latitude+Longitude to ECEF Sparse vector improvements and bug fixes New filter: remove NaN values and missing values New filter: add histogram-based jitter New filter: normalize using statistical distributions New filter: robust standardization using Median and MAD New filter: Linear discriminant analysis (LDA)

Index Layer

  • Another speed up in R-trees
  • Refactoring of M- and R-trees: Support for different strategies in M-tree New strategies for M-tree splits Speedups in M-tree
  • New index structure: in-memory k-d-tree
  • New index structure: in-memory Locality Sensitive Hashing (LSH)
  • New index structure: approximate projected indexes, such as PINN
  • Index support for geodetic data - (Details: Geodetic Distance Queries on R-Trees for Indexing Geographic Data, SSTD13)
  • Sampled k nearest neighbors: reference KDD13 "Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles"
  • Cached (precomputed) k-nearest neighbors to share across multiple runs
  • Benchmarking "algorithms" for indexes

Mathematics and Statistics

  • Many new distributions have been added, now 28 different distributions are supported
  • Additional estimation methods (using advanced statistics such as L-Moments), now 44 estimators are available
  • Trimming and Winsorizing
  • Automatic best-fit distribution estimation
  • Preprocessor using these distributions for rescaling data sets
  • API changes related to the new distributions support
  • More kernel density functions
  • RANSAC covariance matrix builder (unfortunately rather slow)

Visualization

  • 3D projected coordinates (Details: Interactive Data Mining with 3D-Parallel-Coordinate-Trees, SIGMOD2013)
  • Convex hulls now also include nested hierarchical clusters

Other

  • Parser speedups
  • Sparse vector bug fixes and improvements
  • Various bug fixes
  • PCA, MDS and LDA filters
  • Text output was slightly improved (but still needs to be redesigned from scratch - please contribute!)
  • Refactoring of hierarchy classes
  • New heap classes and infrastructure enhancements
  • Classes can have aliases, e.g. "l2" for euclidean distance.
  • Some error messages were made more informative.
  • Benchmarking classes, also for approximate nearest neighbor search.

Logo CIlib Computational Intelligence Library 0.8

by gpampara - August 22, 2013, 08:34:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1430 views, 401 downloads, 1 subscription

About: CIlib is a library of computational intelligence algorithms and supporting components that allows simple extension and experimentation. The library is peer reviewed and is backed by a leading research group in the field. The library is under active development.

Changes:

Initial Announcement on mloss.org.


Logo Apache Mahout 0.8

by gsingers - July 27, 2013, 15:52:32 CET [ Project Homepage BibTeX Download ] 15693 views, 4305 downloads, 2 subscriptions

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

Changes:

Apache Mahout 0.8 contains, amongst a variety of performance improvements and bug fixes, an implementation of Streaming K-Means, deeper Lucene/Solr integration and new scalable recommender algorithms. For a full description of the newest release, see http://mahout.apache.org/.


Logo PREA Personalized Recommendation Algorithms Toolkit 1.1

by srcw - September 1, 2012, 22:53:37 CET [ Project Homepage BibTeX Download ] 7523 views, 1912 downloads, 2 subscriptions

About: An open source Java software providing collaborative filtering algorithms.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Mulan 1.4.0

by lefman - August 1, 2012, 09:49:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14501 views, 6001 downloads, 1 subscription

About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.

Changes:

Learners

  • BinaryRelevance.java: improved data handling that avoids copying the entire input space, leading to important speedups in case of large datasets and very large number of labels.
  • RAkEL.java: updated technical information, added a check for the case where the number of labels is less or equal than the size of the subset.
  • MultiLabelKNN.java: now checks whether the number of instances is less than the number of requested nearest neighbors.
  • Addition of AdaBoostMH.java, an explicit implementation of AdaBoost.MH as combination of AdaBoostM1 and IncludeLabelsClassifier.
  • Addition of MLPTO.java, the Multi Label Probabilistic Threshold Optimizer (MLTPTO) thresholding technique.
  • Addition of ApproximateExampleBasedFMeasureOptimizer.java, an approximate method for the maximization of example-based F-measure.

Measures/Evaluation

  • Addition of Specificity measure (example-based, micro/macro label-based)
  • Addition of Mean Average Interpolated Precision (MAiP), Geometric Mean Average Precision (GMAP), Geometric Mean Average Interpolated Precision (GMAiP).
  • New methods for stratified multi-label evaluation.
  • Added support for outputting per label results for all measures that implement the MacroAverageMeasure interface.
  • Simplifying the "strictness" issue of information retrieval measures, by adopting specific assumptions (outlined in the new class InformationRetrievalMeasures.java) to handle special cases, instead of the less clear and useful solution of outputting NaN and the less realistic solution or ignoring special cases.

Bug fixes

  • Bug fix in LabelsBuilder.java.
  • Bug fix in Ranker.java.
  • Bug-fix in ThresholdPrediction.java.
  • Fix for bug occurring when loading the XSD for mulan data outside the command-line environment (e.g. web applications).
  • Javadoc comment updates.

API changes

  • Upgrade to Java 1.6
  • Upgrade to JUnit 4.10
  • Upgrade to Weka 3.7.6.

Miscellaneous

  • Meaningful messages are now shown when a DataLoadException is thrown.
  • PT6(PT6Transformation.java): renamed to IncludeLabelsTransformation.java.
  • MultiLabelInstances now support serialization, as needed by the improved binary relevance transformation.
  • BinaryRelevanceAttributeEvaluator.java: updated according to latest BR improvements.

Logo MLWizard 5.2

by remat - July 26, 2012, 15:04:14 CET [ Project Homepage BibTeX Download ] 2940 views, 737 downloads, 1 subscription

About: MLwizard recommends and optimizes classification algorithms based on meta-learning and is a software wizard fully integrated into RapidMiner but can be used as library as well.

Changes:

Faster parameter optimization using genetic algorithm with predefined start population.


Logo WebEnsemble 1.0

by jungc005 - May 8, 2012, 22:24:44 CET [ BibTeX Download ] 1400 views, 480 downloads, 1 subscription

About: Use the power of crowdsourcing to create ensembles.

Changes:

Initial Announcement on mloss.org.


Logo MLFlex 02-21-2012-00-12

by srp33 - April 3, 2012, 16:44:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2070 views, 418 downloads, 1 subscription

About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.)

Changes:

Initial Announcement on mloss.org.


Logo NaN toolbox 2.5.2

by schloegl - February 10, 2012, 11:45:52 CET [ Project Homepage BibTeX Download ] 30377 views, 6166 downloads, 1 subscription

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes:

Changes in v.2.5.2 - faster version of quantile if multiple quantiles are requested - removes the dependency on ZLIB and thus - fixes "pkg install nan" for Octave on Windows - a number of minor improvements

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG


Logo mldata.org svn-r1070-Apr-2011

by sonne - April 8, 2011, 10:15:49 CET [ Project Homepage BibTeX Download ] 3850 views, 725 downloads, 1 subscription

About: The source code of the mldata.org site - a community portal for machine learning data sets.

Changes:

Initial Announcement on mloss.org.


Showing Items 1-20 of 25 on page 1 of 2: 1 2 Next