A learning scheme for a fuzzy k-NN rule
نویسنده
چکیده
The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership-array W[I, mR], where l and m R denote the number of classes and the number of elements in the reference set X R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimated by the 'leaving one out' method.
منابع مشابه
A hybridization of evolutionary fuzzy systems and ant Colony optimization for intrusion detection
A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate t...
متن کاملNEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...
متن کاملPii: S0045-7906(99)00027-0
Neural networks (NN), genetic algorithms (GA), and genetic programming (GP) are augmented with fuzzy logic-based schemes to enhance arti®cial intelligence of automated systems. Such hybrid combinations exhibit added reasoning, adaptation, and learning ability. In this expository article, three dominant hybrid approaches to intelligent control are experimentally applied to address various roboti...
متن کاملSemi-supervised Learning by Fuzzy Clustering and Ensemble Learning
This paper proposes a semi-supervised learning method using Fuzzy clustering to solve word sense disambiguation problems. Furthermore, we reduce side effects of semi-supervised learning by ensemble learning. We set classes for labeled instances. The -th labeled instance is used as the prototype of the -th class. By using Fuzzy clustering for unlabeled instances, prototypes are moved to more sui...
متن کاملA TS Fuzzy Model Derived from a Typical Multi-Layer Perceptron
In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modify...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Pattern Recognition Letters
دوره 1 شماره
صفحات -
تاریخ انتشار 1983