Comparison of learning and generalization capabilities of the Kak and the backpropagation algorithms
Information Sciences—Intelligent Systems: An International Journal
The nature of statistical learning theory
The nature of statistical learning theory
The potential use of DEA for credit applicant acceptance systems
Computers and Operations Research - Special issue on data envelopment analysis
Data mining: concepts and techniques
Data mining: concepts and techniques
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Customized classification learning based on query projections
Information Sciences: an International Journal
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Data gravitation based classification
Information Sciences: an International Journal
Supplier selection: A hybrid model using DEA, decision tree and neural network
Expert Systems with Applications: An International Journal
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications: An International Journal
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
A hybrid radial basis function and data envelopment analysis neural network for classification
Computers and Operations Research
| Hi-index | 0.07 |
This paper establishes the equivalent relationship between the data classification machine and the data envelopment analysis (DEA) model, and thus build up a DEA based classification machine. A data is characterized by a set of values. Without loss of the generality, it is assumed that the data with a set of smaller values is preferred. The classification is to label if a particular data belongs to a specified group according to a set of predetermined characteristic or attribute values. We treat such a data as a decision making unit (DMU) with these given attribute values as input and an artificial output of identical value 1. Then classifying a data is equivalent to testing if the DMU is in the production possibility set, called acceptance domain, constructed by a sample training data set. The proposed DEA classification machine consists of an acceptance domain and a classification function. The acceptance domain is given by an explicit system of linear inequalities. This makes the classification process computationally convenient. We then discuss the preference cone restricted classification process. The method can be applied to classifying large amount of data. Furthermore, the research finds that DEA classification machines based on different DEA models have the same format. Input-oriented and output-oriented DEA classification machines have similar properties. The method developed has great potential in practice with its computational efficiency.