Combining instance selection and self-training to improve data stream quantification
نویسندگان
چکیده
منابع مشابه
Instance Selection to Improve Gamma Classifier
Pre-processing the dataset is an important stage in the Knowledge Discovery in Datasets (KDD) process. Filtering noise through instance selection is a necessary task. With this, the risk to use misclassified and non-representative instances to train supervised classifiers is reduced. This study aims at improving the performance of the Gamma associative classifier, by introducing a novel similar...
متن کاملData point selection for self-training
Problems for parsing morphologically rich languages are, amongst others, caused by the higher variability in structure due to less rigid word order constraints and by the higher number of different lexical forms. Both properties can result in sparse data problems for statistical parsing. We present a simple approach for addressing these issues. Our approach makes use of self-training on instanc...
متن کاملCombining Data Clusterings with Instance Level Constraints
Recent work has focused the incorporation of a priori knowledge into the data clustering process, in the form of pairwise constraints, aiming to improve clustering quality and find appropriate clustering solutions to specific tasks or interests. In this work, we integrate must-link and cannot-link constraints into the cluster ensemble framework. Two algorithms for combining multiple data partit...
متن کاملCombining Instance Weighting and Fine Tuning for Training Naïve Bayesian Classifiers with Scant data
This work addresses the problem of having to train a Naïve Bayesian classifier using limited data. It first presents an improved instance-weighting algorithm that is accurate and robust to noise and then it shows how to combine it with a fine tuning algorithm to achieve even better classification accuracy. Our empirical work using 49 benchmark data sets shows that the improved instance-weightin...
متن کاملCombining Feature Selection and Feature Construction to Improve Concept Learning for High Dimensional Data
This paper describes and experimentally analyses a new dimension reduction method for microarray data. Microarrays, which allow simultaneous measurement of the level of expression of thousands of genes in a given situation (tissue, cell or time), produce data which poses particular machine-learning problems. The disproportion between the number of attributes (tens of thousands) and the number o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Brazilian Computer Society
سال: 2018
ISSN: 0104-6500,1678-4804
DOI: 10.1186/s13173-018-0076-0