Add to ORKGClassifier selection process implies mastering a lot of background information on the dataset, the model and the algorithms in question. We suggest that a recommender system can reduce this effort by registering background information and the knowledge of the expert. In this study we propose such a system and take a first look on how it can be done. We compare various classifiers against different datasets and then come up with the most appropriate classifier for a particular dataset based on its unique characteristic. Keywords: Data mining, algorithm selection, UCI, Wek
Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of in...
Non-expert users find complex to gain richer insights into the increasingly amount of available data...
The Algorithm Selection Problem is to select the most appropriate way for solving a problem given a ...
Classifier selection process implies mastering a lot of background information on the dataset, the m...
A lot of classification algorithms are available in the area of data mining for solving the same kin...
This paper introduces a new method for learning algorithm evaluation and selection, with empirical r...
Recommender systems are very important in searching for items all over the internet. There are many ...
The increasing availability of data gatherable from various sources and in several contexts, is forc...
Abstract In this chapter, we give an overview of the main Data Mining techniques used in the context...
In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algori...
Recommendation systems now days are the heart of success stories for business and optimization of re...
Feature selection plays an important role in machine learning or data mining problems. Removing irre...
This timely book presents Applications in Recommender Systems which are making recommendations using...
In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algori...
International audienceOver the last decade, several algorithms for process discovery and process con...
Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of in...
Non-expert users find complex to gain richer insights into the increasingly amount of available data...
The Algorithm Selection Problem is to select the most appropriate way for solving a problem given a ...
Classifier selection process implies mastering a lot of background information on the dataset, the m...
A lot of classification algorithms are available in the area of data mining for solving the same kin...
This paper introduces a new method for learning algorithm evaluation and selection, with empirical r...
Recommender systems are very important in searching for items all over the internet. There are many ...
The increasing availability of data gatherable from various sources and in several contexts, is forc...
Abstract In this chapter, we give an overview of the main Data Mining techniques used in the context...
In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algori...
Recommendation systems now days are the heart of success stories for business and optimization of re...
Feature selection plays an important role in machine learning or data mining problems. Removing irre...
This timely book presents Applications in Recommender Systems which are making recommendations using...
In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algori...
International audienceOver the last decade, several algorithms for process discovery and process con...
Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of in...
Non-expert users find complex to gain richer insights into the increasingly amount of available data...
The Algorithm Selection Problem is to select the most appropriate way for solving a problem given a ...