Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning
نویسندگان
چکیده
Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy.
منابع مشابه
IRDDS: Instance reduction based on Distance-based decision surface
In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
متن کاملCycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation
Cycle time optimization could be one of the great challenges in business process management. Although there is much research on this subject, task similarities have been paid little attention. In this paper, a new approach is proposed to optimize cycle time by minimizing entropy of work lists in resource allocation while keeping workloads balanced. The idea of the entropy of work lists comes fr...
متن کاملTransition Potential Modeling of Land-Cover based on Similarity Weighted Instance-based Learning Procedure and Its Implication in the REDD Project Design Document
Reducing Emissions from Deforestation and Forest Degradation (REDD) is a climate change mitigation strategy employed to reduce the intensity of deforestation and GHGS emissions. In recent decades, drastic land use changes in Mazandaran province caused a substantial reduction in the amount of Hyrcanian forests. The present research based on objectives of REDD projects paid to identify of fore...
متن کاملHigh training set size reduction by space partitioning and prototype abstraction
Instance-based learning methods like the nearest neighbour classiÿer generally suuer from the indiscriminate storage of all training instances, resulting in large memory requirements and slow execution speed. In this paper, new training set size reduction methods based on prototype generation and space partitioning are proposed. Experimental results show that the new algorithms achieve a very h...
متن کاملIFSB-ReliefF: A New Instance and Feature Selection Algorithm Based on ReliefF
Increasing the use of Internet and some phenomena such as sensor networks has led to an unnecessary increasing the volume of information. Though it has many benefits, it causes problems such as storage space requirements and better processors, as well as data refinement to remove unnecessary data. Data reduction methods provide ways to select useful data from a large amount of duplicate, incomp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2006