Instance-Based Learning Algorithms
Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Data mining: concepts and techniques
Data mining: concepts and techniques
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A cognitive vision approach to early pest detection in greenhouse crops
Computers and Electronics in Agriculture
Segregating Confident Predictions of Chemicals' Properties for Virtual Screening of Drugs
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The use of features selection and nearest neighbors rule for faults diagnostic in induction motors
Engineering Applications of Artificial Intelligence
Biomarker discovery for toxicity
Neurocomputing
Feature Selection for Accelerometer-Based Posture Analysis in Parkinson's Disease
IEEE Transactions on Information Technology in Biomedicine
A new approach to aflatoxin detection in chili pepper by machine vision
Computers and Electronics in Agriculture
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Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. ssp. durum, 2n=28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge.