نتایج جستجو برای: rough extreme learning machine
تعداد نتایج: 842991 فیلتر نتایج به سال:
One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...
Extreme Learning Machine is a fast single layer feed forward neural network for real valued classification. It suffers from the problem of instability and over fitting. Voting based Extreme Learning Machine, VELM reduces this performance variation in Extreme Learning Machine by employing majority voting based ensembling technique. VELM improves the performance of ELM at the cost of increased re...
This paper reports a study to identify static software reuse potential metrics that can be used to classify C source code into reusable and non-reusable classes. The techniques used exploit a decision tree inductive machine learning and rough sets theory. The results we obtained show that the former technique, as implemented by C4.5, produces a much more accurate set of classification rules tha...
Since the early 1990s, many authors have studied fuzzy rough set models and their application in machine learning and data reduction. In this work, we adjust the β-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increases. Furthermore, we evaluate the robustness of the new models a-β-PREC and a-OWA to noisy data and...
This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution. ETM+ multispectral data set (Engla...
In this paper, the main techniques of inductive machine learning are united to the knowledge reduction theory based on rough sets from the theoretical point of view. And then the Monk’s problems are resolved again employing rough sets. As far as accuracy and conciseness are concerned, the learning algorithms based on rough sets have remarkable superiority to the previous methods.
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