scholarly journals A High Accurate Multiple Classifier System for Entity Resolution Using Resampling and Ensemble Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Zhou Xing ◽  
Diao Xingchun ◽  
Cao Jianjun
Keyword(s):  
Empirical Study ◽  
State Of The Art ◽  
Entity Resolution ◽  
High Accuracy ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifiers ◽  
Ensemble Selection ◽  
Multiple Classifier ◽  
Optimal Resampling

Classifiers are often used in entity resolution to classify record pairs into matches, nonmatches, and possible matches, the performance of classifiers is directly related to the performance of entity resolution. In this paper, we develop a multiple classifier system using resampling and ensemble selection. We make full use of the characteristics of entity resolution to distinguish ambiguous instances before classification, so that the algorithm can focus on the ambiguous instances in parallel. Instead of developing an empirical optimal resampling ratio, we vary the ratio in a range to generate multiple resampled data. Further, we use the resampled data to train multiple classifiers and then use ensemble selection to select the best classifiers subset, which is also the best resampling ratio combination. Empirical study shows our method has a relatively high accuracy compared to other state-of-the-art multiple classifiers systems.

FINGERPRINT RECOGNITION USING MULTIPLE CLASSIFIER SYSTEM

Fractals ◽  
2007 ◽  
Vol 15 (03) ◽  
pp. 273-278 ◽  
Author(s):  
RAJIV KUMAR NATH
Keyword(s):  
Pattern Recognition ◽  
Clustering Algorithm ◽  
Fourier Transforms ◽  
Test Sample ◽  
Classifier Fusion ◽  
Multiple Sources ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifiers ◽  
Multiple Classifier

In this paper, the human fingerprint, which is independent of rotation and scaling, is recognized. The multiple classification technique, based on wavelet and fractal analysis, is used. It is shown that systematic incorporation of decision from various classifiers leads to a better decision rather than simply fusing them. Multiple classifiers can serve as a means of enhancing the performance of pattern recognition problems. Multiple classifier system design involves the problem of classifier fusion. This paper deals with multi-classifier systems in which each classifier uses its own representation of the input pattern, based on features collected from multiple sources. The multiple feature sources considered here are multi-fractals, wavelets and fast Fourier transforms coefficients. A clustering algorithm is used to observe the efficacy of the feature sources. The multiple sources were graded according to their effectiveness of providing more non-overlapping clusters for different groups into which the samples are to be separated. This approach first considers the best source for the feature parameters. If this feature classifies the test sample into more than one cluster, then the feature next to the best is summoned to finish up the remaining part of the classification process. The continuation of this process along with the judicious selection of classifiers succeeds in identifying a single cluster for the test sample. The results obtained after the experiments on a set of fingerprint images shows that this novel technique can go a long way in avoiding ambiguity and thus limiting the need for use of soft-computing tools for making decisions. Our method provides a hard, concrete and accurate solution to pattern recognition problems employing multiple classifiers.

The Application of Multiple Classifier System for Environmental Audio Classification

2013 ◽  
Vol 462-463 ◽  
pp. 225-229 ◽  
Author(s):  
Yan Zhang ◽  
Dan Jv Lv ◽  
Hong Song Wang
Keyword(s):  
Random Forest ◽  
Data Classification ◽  
Experimental Results ◽  
Audio Classification ◽  
Key Factors ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifiers ◽  
Multiple Classifier ◽  
Audio Data

Multiple classifier system trains different classifiers and combines their predictions to improve the accuracy of classification. This paper explains the popular algorithms and strategies in multiple classifier system, and points out the key factors to affect the performance of the application of multiple classifier system. The experiments are carried out on given environmental audio data in order to compare the singular classifier methods with multiple classifier system such as Random Forest and MCS, as well as Bagging and AdaBoost. The experimental results show that the multiple classifiers technology outperforms the singular classifier and obtains better performance in environmental audio data classification. It provides an effective way to guarantee the performance and generalization of classification.

Genders and classifiers in Kampa (Arawak) languages of Peru

2019 ◽  
pp. 30-66
Author(s):  
Elena I. Mihas
Keyword(s):  
Syntactic Parsing ◽  
Classifier Systems ◽  
Gender Agreement ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Number Words ◽  
Multiple Classifiers ◽  
Noun Incorporation ◽  
Multiple Classifier ◽  
Diachronic Development

This chapter examines the semantics, morphosyntax, and functions of the gender and classifier systems of Kampa Arawak languages of Peru. All Kampa languages have genders and classifiers. Their origin and diachronic development are different. Gender agreement morphology comes from the pan-Kampa verbal person markers. The sources of multiple classifiers are bound nouns inflected on the pattern of obligatorily possessed nouns, unbound nouns, and bound verb roots; these are considered in the context of compounding and noun incorporation. Gender marking is mandatory and exhaustive, being reflected in the agreement marking on noun modifiers (adjectives, demonstratives, possessor NP), possessive pronouns, demonstrative identifiers, personal pronouns, verbs, and coordinating operators. Multiple classifiers show less exuberant distribution, occurring on nouns, verbs, number words, and adjectives. Classifiers are neither sensitive to gender nor animacy. Classifiers are semantically motivated, showing semantic agreement with controller nouns. The multiple classifier system does not participate in syntactic parsing of constituents via morphological agreement. The main purpose of their use is pragmatic.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

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