Multi-source and multi-classifier system for regional landcover mapping

2004 ◽  
Author(s):  
C.K. Brewer ◽  
J.A. Barber ◽  
G. Willhauck ◽  
U.C. Benz
Keyword(s):  
Classifier System

Identification of Anti-cancer Peptides Based on Multi-classifier System

2020 ◽  
Vol 22 (10) ◽  
pp. 694-704 ◽  
Author(s):  
Wanben Zhong ◽  
Bineng Zhong ◽  
Hongbo Zhang ◽  
Ziyi Chen ◽  
Yan Chen
Keyword(s):  
Machine Learning ◽  
Side Effect ◽  
Learning Models ◽  
Normal Cells ◽  
Classifier System ◽  
Prediction Rate ◽  
Anti Cancer ◽  
Feature Information ◽  
Machine Learning Models ◽  
Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

scholarly journals The gender system of Longuda

2021 ◽  
Vol 74 (2) ◽  
pp. 327-346
Author(s):  
Julius-Maximilian Elstermann ◽  
Ines Fiedler ◽  
Tom Güldemann
Keyword(s):  
Historical Reconstruction ◽  
Classifier System ◽  
Class System ◽  
Nominal Form ◽  
Noun Class ◽  
Nominal Classification ◽  
Gender System

Abstract This article describes the gender system of Longuda. Longuda class marking is alliterative and does not distinguish between nominal form and agreement marking. While it thus appears to be a prototypical example of a traditional Niger-Congo “noun-class” system, this identity of gender encoding makes it look morpho-syntactic rather than lexical. This points to a formerly independent status of the exponents of nominal classification, which is similar to a classifier system and thus less canonical. Both types of class marking hosts involve two formally and functionally differing allomorphs, which inform the historical reconstruction of Longuda noun classification in various ways.

Learning classification rules from an ion chromatography database using a genetic based classifier system

1997 ◽  
Vol 344 (1-2) ◽  
pp. 1-15 ◽  
Author(s):  
A.H.C. van Kampen ◽  
Z. Ramadan ◽  
M. Mulholland ◽  
D.B. Hibbert ◽  
L.M.C. Buydens
Keyword(s):  
Ion Chromatography ◽  
Classification Rules ◽  
Classifier System

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.

SELF-ADAPTIVE LEARNING CLASSIFIER SYSTEM

2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD
Keyword(s):  
Mutation Rate ◽  
Adaptive Learning ◽  
Adaptive Mutation ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Adaptive Parameters ◽  
Proposed Model ◽  
Fixed Parameter ◽  
Self Adaptive

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

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