kNN classifier is widely used in text categorization, however, kNN has the large computational and store requirements, and its performance also suffers from uneven distribution of training data. Usually, condensing te...
详细信息
ISBN:
(纸本)9780769531311
kNN classifier is widely used in text categorization, however, kNN has the large computational and store requirements, and its performance also suffers from uneven distribution of training data. Usually, condensing technique is resorted to reducing the noises of training data and decreasing the cost of time and space. Traditional condensing technique picks up samples in a random manner when initialization. Though random sampling is one means to reduce outliers, the extremely stochastic may lead to bad performance sometimes, that is, advantages of sampling may be suppressed To avoid such a misfortune, we propose a variation of traditional condensing technique. Experiment results illustrate this strategy can solve above problems effectively.
The paper presents the process of taking global decisions on the basis of the knowledge of local decision systems, in which sets of conditional attributes are different but not necessarily disjoint. We propose the org...
详细信息
The paper presents the process of taking global decisions on the basis of the knowledge of local decision systems, in which sets of conditional attributes are different but not necessarily disjoint. We propose the organization of local decision systems into a multi-agent system with a hierarchical structure. The structure of multi-agent systems and the theoretical aspects of the organization of the system are presented. An editing and a condensing algorithm have been used in the process of global decision making. Also a density-based algorithm has been used in the process of taking global decisions to resolve conflicts. Furthermore, the paper presents the results of experiments conducted using some data sets from UCI repository.
As a simple and effective classification approach, KNN is widely used in text categorization. However, KNN classifier not only has the large computational and store requirements, but also deteriorates performance of c...
详细信息
ISBN:
(纸本)9781424409723
As a simple and effective classification approach, KNN is widely used in text categorization. However, KNN classifier not only has the large computational and store requirements, but also deteriorates performance of classification because of uneven distribution of training data. In this paper, we present a combinational technique, multi-edit-nearest-neighbor and condensing techniques, for reducing the noises of training data and decreasing the cost of time and space. Our experiment results illustrate that this strategy can solve above problems effectively.
As a simple and effective classification approach, KNN is widely used in text ***, KNN classifier not only has the large computational and store requirements, but also deteriorates performance of classification becaus...
详细信息
As a simple and effective classification approach, KNN is widely used in text ***, KNN classifier not only has the large computational and store requirements, but also deteriorates performance of classification because of uneven distribution of training *** this paper, we present a combinational technique, multi-edit-nearesl-neighbor and condensing techniques, for reducing the noises of training data and decreasing the cost of time and *** experiment results illustrate that this strategy can solve above problems effectively.
In this paper we address the elusive problem of selecting references (templates) for minimum distance classification when the number of pattern classes is very large. We argue that the multiedit/condensing technique o...
详细信息
In this paper we address the elusive problem of selecting references (templates) for minimum distance classification when the number of pattern classes is very large. We argue that the multiedit/condensing technique offers an automatic solution to this problem which avoids the proliferation of references without impairing the recognition performance. The effectiveness of the approach is demonstrated by experimental results in a print recognition context. Suggestions are made about ways of circumventing problems of computational complexity.
暂无评论