scholarly journals SELF-ORGANIZING NEURAL GROVE: EFFICIENT NEURAL NETWORK ENSEMBLES USING PRUNED SELF-GENERATIONG NEURAL TREES

2014 ◽  
pp. 210-216
Author(s):  
Hirotaka Inoue ◽  
Kyoshiro Sugiyama
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
The Self ◽  
Learning Ability ◽  
Support Vector ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Computation Cost ◽  
Multiple Classifier ◽  
Self Organizing

R ecently, mul tiple classifier systems have been used for practical applications to improve classification accuracy. Self-generating neural networks are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on self-generating neural networks increases in proportion to the numbers of self-gene rating neural networks. In this paper, w e propose a novel prunin g method for efficient classification and we call this model a self-organizing neural grove. Experiments have been conducted to compare the self-organizing neural grove with bagging and the self-organizing neural grove with boosting, and support vector machine. The results show that the self-organizing neural grove can improve its classification accuracy as well as reducing the computation cost.

scholarly journals Automatic Classification of Remote Sensing Images Using Multiple Classifier Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Yang ◽  
Chunxiang Cao ◽  
Ying Xing ◽  
Xiaowen Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Ground Truth ◽  
Support Vector ◽  
Accuracy Evaluation ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Remote Sensing Images ◽  
Analytic Hierarchy ◽  
Multiple Classifier

It is a challenge to obtain accurate result in remote sensing images classification, which is affected by many factors. In this paper, aiming at correctly identifying land use types reflec ted in remote sensing images, support vector machine, maximum likelihood classifier, backpropagation neural network, fuzzy c-means, and minimum distance classifier were combined to construct three multiple classifier systems (MCSs). Two MCSs were implemented, namely, comparative major voting (CMV) and Bayesian average (BA). One method called WA-AHP was proposed, which introduced analytic hierarchy process into MCS. Classification results of base classifiers and MCSs were compared with the ground truth map. Accuracy indicators were computed and receiver operating characteristic curves were illustrated, so as to evaluate the performance of MCSs. Experimental results show that employing MCSs can increase classification accuracy significantly, compared with base classifiers. From the accuracy evaluation result and visual check, the best MCS is WA-AHP with overall accuracy of 94.2%, which overmatches BA and rivals CMV in this paper. The producer’s accuracy of each land use type proves the good performance of WA-AHP. Therefore, we can draw the conclusion that MCS is superior to base classifiers in remote sensing image classification, and WA-AHP is an efficient MCS.

scholarly journals Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study

2021 ◽  
Author(s):  
Mario Barbareschi ◽  
Salvatore Barone ◽  
Nicola Mazzocca
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Approximate Computing ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Area Overhead ◽  
Multiple Classifier ◽  
Field Programmable ◽  
Design Paradigm ◽  
Approximate Decision

AbstractSo far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit approximate computing design paradigm to trade hardware area overhead off for classification accuracy. In particular, starting from trained DT models and employing precision-scaling technique, we explore approximate decision tree variants by means of multiple objective optimization problem, demonstrating a significant performance improvement targeting field-programmable gate array devices.

The Research of Chinese Text Automatic Classification Based on Multiple

2012 ◽  
Vol 263-266 ◽  
pp. 1543-1548
Author(s):  
Sheng Li Zhang
Keyword(s):  
Chinese Text ◽  
Large Scale ◽  
Recall Rate ◽  
Support Vector ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Large Scale Data ◽  
Multiple Classifier ◽  
A New Technique ◽  
Large Scale Data Processing

Support Vector Machines (SVMs) is a new technique for data mining. It has wide applications in various fields and is a research hot pot of the machine learning field, but, being applied to handling large-scale problems, SVMs needs longer t raining time and larger memory. It’s an effective way to solve large scale data processing in text classification with multiple classifier systems composed by multiple support vector machine classifiers. Based on the analysis of traditional parallel algorithms, this paper proposes an improved algorithm based on multiple SVMs. The experimental results indicate that the new algorithm works well in precision and recall rate in the condition that the speeds of classification increase remarkably. Compared with traditional algorithms, the classified accuracy is lower but is within the range for acceptance.

scholarly journals Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Radhika Kamath ◽  
Mamatha Balachandra ◽  
Srikanth Prabhu
Keyword(s):  
Computer Vision ◽  
Weed Management ◽  
Digital Images ◽  
Decision Function ◽  
Support Vector ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Weed Detection ◽  
Detection And Identification ◽  
Multiple Classifier

Weeds are unwanted plants that grow among crops. These weeds can significantly reduce the yield and quality of the farm output. Unfortunately, site-specific weed management is not followed in most of the cases. That is, instead of treating a field with a specific type of herbicide, the field is treated with a broadcast herbicide application. This broadcast application of the herbicide has resulted in herbicide-resistant weeds and has many ill effects on the natural environment. This has prompted many research studies to seek the most effective weed management techniques. One such technique is computer vision-based automatic weed detection and identification. Using this technique, weeds can be detected and identified and a suitable herbicide can be recommended to the farmers. Therefore, it is important for the computer vision technique to successfully identify and classify the crops and weeds from the digital images. This paper investigates the multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images. Digital images of paddy crops and weeds from the paddy fields were acquired using three different cameras fixed at different heights from the ground. Texture, color, and shape features were extracted from the digital images after background subtraction and used for classification. A simple and new method was used as a decision function in the multiple classifier systems. An accuracy of 91.36% was obtained by the multiple classifier systems and was found to outperform single classifier systems.

HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

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