Abstract
Feature selection and classification techniques have been studied independently without considering the interaction between both procedures, which leads to a degraded performance. In this paper, we present a new neural network approach, which is called an algorithm learning based neural network (ALBNN), to improve classification accuracy by integrating feature selection and classification procedures. In general, a knowledge-based artificial neural network operates on prior knowledge from domain experience, which provides it with better starting points for the target function and leads to better classification accuracy. However, prior knowledge is usually difficult to identify. Instead of using unknown background resources, the proposed method utilizes prior knowledge that is mathematically calculated from the properties of other learning algorithms such as PCA, LARS, C4.5, and SVM. We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning. ALBNN correctly approximates a target hypothesis by both considering the interaction between two procedures and minimizing individual procedure errors. The approach produces new relevant features and improves the classification accuracy. Experimental results exhibit improved performance in various classification problems. ALBNN can be applied to various fields requiring high classification accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 231-241 |
| Number of pages | 11 |
| Journal | Expert Systems with Applications |
| Volume | 40 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2013 Jan 1 |
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Keywords
- Algorithm learning based neural network (ALBNN)
- Classification
- Extreme learning machine
- Feature selection
- Neural network
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Engineering(all)
Cite this
Algorithm learning based neural network integrating feature selection and classification. / Yoon, Hyunsoo; Park, Cheong Sool; Kim, Jun Seok; Baek, Jun-Geol.
In: Expert Systems with Applications, Vol. 40, No. 1, 01.01.2013, p. 231-241.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Algorithm learning based neural network integrating feature selection and classification
AU - Yoon, Hyunsoo
AU - Park, Cheong Sool
AU - Kim, Jun Seok
AU - Baek, Jun-Geol
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Feature selection and classification techniques have been studied independently without considering the interaction between both procedures, which leads to a degraded performance. In this paper, we present a new neural network approach, which is called an algorithm learning based neural network (ALBNN), to improve classification accuracy by integrating feature selection and classification procedures. In general, a knowledge-based artificial neural network operates on prior knowledge from domain experience, which provides it with better starting points for the target function and leads to better classification accuracy. However, prior knowledge is usually difficult to identify. Instead of using unknown background resources, the proposed method utilizes prior knowledge that is mathematically calculated from the properties of other learning algorithms such as PCA, LARS, C4.5, and SVM. We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning. ALBNN correctly approximates a target hypothesis by both considering the interaction between two procedures and minimizing individual procedure errors. The approach produces new relevant features and improves the classification accuracy. Experimental results exhibit improved performance in various classification problems. ALBNN can be applied to various fields requiring high classification accuracy.
AB - Feature selection and classification techniques have been studied independently without considering the interaction between both procedures, which leads to a degraded performance. In this paper, we present a new neural network approach, which is called an algorithm learning based neural network (ALBNN), to improve classification accuracy by integrating feature selection and classification procedures. In general, a knowledge-based artificial neural network operates on prior knowledge from domain experience, which provides it with better starting points for the target function and leads to better classification accuracy. However, prior knowledge is usually difficult to identify. Instead of using unknown background resources, the proposed method utilizes prior knowledge that is mathematically calculated from the properties of other learning algorithms such as PCA, LARS, C4.5, and SVM. We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning. ALBNN correctly approximates a target hypothesis by both considering the interaction between two procedures and minimizing individual procedure errors. The approach produces new relevant features and improves the classification accuracy. Experimental results exhibit improved performance in various classification problems. ALBNN can be applied to various fields requiring high classification accuracy.
KW - Algorithm learning based neural network (ALBNN)
KW - Classification
KW - Extreme learning machine
KW - Feature selection
KW - Neural network
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U2 - 10.1016/j.eswa.2012.07.018
DO - 10.1016/j.eswa.2012.07.018
M3 - Article
AN - SCOPUS:84866123983
VL - 40
SP - 231
EP - 241
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 1
ER -