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.

• Added almost 20 more conversions and 20 new actors
• R-Project integration using Rserve
• WEKA webservice allows for programming language agnostic training, evaluation and use of WEKA models (classifiers, clusterers) and data processing using filters
• Spreadsheets now come with basic formula support
• Spreadsheets can be used for lookup tables in the flow
• Support for "chunked" reading/writing of spreadsheets to process millions of rows

About: MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.

Important changes:

• new rating predictor GSVD++ (contributed by Marcelo Manzato)
• new recommenders ExternalRatingPredictor and ExternalItemRecommender to evaluate external tools with the MyMediaLite evaluation framework
• incremental update support for item recommendation UserKNN and ItemKNN (based on a pull request by João Vinagre)
• --cross-validation support for the rating_based_ranking tool (as requested by Pieter-Jan Verbrugen)
• removed the group recommendation code
• cleaner item recommendation evaluation, with a bug fix in the cross-validation code and a complete rewrite of online evaluation
• removed unused matrix and vector math, faster and simplified matrix code

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.)

Initial Announcement on mloss.org.

About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman.

Initial Announcement on mloss.org.

About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman.

Initial Announcement on mloss.org.

About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression.

Initial Announcement on mloss.org.