A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Health monitoring of time-varying stochastic structures by latent components and fuzzy expert system

In this paper, a novel parametric model-based decomposition method is proposed for structural health monitoring of time-varying structures. For this purpose, the advanced Functional-Series Time-dependent Auto Regressive Moving Average （FS-TARMA） technique is used to estimate the parameters and innovation variance used in the parametric signal decomposition scheme. Additionally, a unique feature extraction and reduction method based on the decomposed signals, known as Latent Components （LCs）, is proposed. To evaluate the efficiency of the proposed method, numerical simulation and an experimental study in the laboratory were conducted on a time-varying structure, where various types of damage were introduced. The Fuzzy Expert System （FES） was used as a classification toot to demonstrate that the proposed method successfully identifies different structural conditions when compared with other methods based on non-reduced and ordinary feature extraction.

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

Knowledge Based Pipeline Network Classification and Recognition Method of Maps

Map recognition is an essential data input means of Geographic Information System (GIS). How to solve the problems in the procedure, such as recognition of maps with crisscross pipeline networks, classification of buildings and roads, and processing of connected text, is a critical step for GIS keeping high-speed development. In this paper, a new recognition method of pipeline maps is presented, and some common patterns of pipeline connection and component labels are established. Through pattern matching, pipelines and component labels are recognized and peeled off from maps. After this approach, maps simply consist of buildings and roads, which are recognized and classified with fuzzy classification method. In addition, the Double Sides Scan (DSS) technique is also described, through which the effect of connected text can be eliminated.