The last chapter of the three-part series on Evolutionary Feature Selection with Big datasets. We will address some fundamental design aspects of a Genetic Algorithm (GA) and commonly chosen options, to then move on to the CHC algorithm and a distributed approach for Feature Selection.
The key to the success of these methods lies in the rich representations these models build, which are generated after an exhaustive and computationally expensive learning process. Deep learning is starting to become essential to specialized areas of business. Nonetheless, it is not the panacea…
Data has become for many companies their most important asset. Ensuring quality of the information, as well as carrying out a proper management and governance of the data, is absolutely essential.
The Agile Team of Stratio went to different events where they showed and explained the Argos Framework to the Agile Community: AOS (Agile Open Space 2017), BBVAgile Conference and CAS (Conference Agile Spain).
Argos is new Stratio’s framework. It is created to develop independent products which are integrated into a single final product. This unique framework also gives importance to dependencies and to refinements.
Big Data Spain is one of the three largest conferences of its kind in Europe. The conference focuses primarily on Big Data, Artificial Intelligence, Cloud Technologies and Digital Transformation. #BDS17 will take place this very 15, 16 and 17 of November right here in sunny Madrid.
This is the second chapter of a three-part series on Evolutionary Feature Selection with Big datasets. We will start where we left off, namely with a review of existing metaheuristics with special focus on Genetic Algorithms.
Spark Streaming is one of the most widely used frameworks for real time processing in the world with Apache Flink, Apache Storm and Kafka Streams. However, when compared to the others, Spark Streaming has more performance problems and its process is through time windows instead of event by event, resulting in delay.