Evolutionary Multiobjective Knowledge Extraction for High-Dimensional Pattern Classification Problems

2004 ◽  
pp. 1123-1132 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Satoshi Namba
Keyword(s):  
Pattern Classification ◽  
Knowledge Extraction ◽  
High Dimensional ◽  
Classification Problems

scholarly journals A new approach to multiple class pattern classification with random matrices

2005 ◽  
Vol 2005 (3) ◽  
pp. 165-175 ◽  
Author(s):  
Dong Q. Wang ◽  
Mengjie Zhang
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Pattern Classification ◽  
Likelihood Estimation ◽  
High Dimensional ◽  
Classification Problems ◽  
Feed Forward Neural Network ◽  
Neural Network Approach ◽  
New Approach ◽  
Multiple Class

We describe a new approach to multiple class pattern classification problems with noise and high dimensional feature space. The approach uses a random matrix X which has a specified distribution with mean M and covariance matrix rij(Σs+Σε) between any two columns of X. When Σε is known, the maximum likelihood estimators of the expectation M, correlation Γ, and covariance Σs can be obtained. The patterns with high dimensional features and noise are then classified by a modified discriminant function according to the maximum likelihood estimation results. This new method is compared with a multilayer feed forward neural network approach on nine digit recognition tasks of increasing difficulty. Both methods achieved good results for those classification tasks, but the new approach was more effective and more efficient than the neural network method for difficult problems.

Locally Adaptive Techniques for Pattern Classification

2011 ◽  
pp. 1170-1175
Author(s):  
Carlotta Domeniconi ◽  
Dimitrios Gunopulos
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
Pattern Classification ◽  
Credit Card ◽  
Curse Of Dimensionality ◽  
Ease Of Use ◽  
Practical Significance ◽  
High Dimensional ◽  
Finite Samples ◽  
Recent Developments

Pattern classification is a very general concept with numerous applications ranging from science, engineering, target marketing, medical diagnosis and electronic commerce to weather forecast based on satellite imagery. A typical application of pattern classification is mass mailing for marketing. For example, credit card companies often mail solicitations to consumers. Naturally, they would like to target those consumers who are most likely to respond. Often, demographic information is available for those who have responded previously to such solicitations, and this information may be used in order to target the most likely respondents. Another application is electronic commerce of the new economy. E-commerce provides a rich environment to advance the state-of-the-art in classification because it demands effective means for text classification in order to make rapid product and market recommendations. Recent developments in data mining have posed new challenges to pattern classification. Data mining is a knowledge discovery process whose aim is to discover unknown relationships and/or patterns from a large set of data, from which it is possible to predict future outcomes. As such, pattern classification becomes one of the key steps in an attempt to uncover the hidden knowledge within the data. The primary goal is usually predictive accuracy, with secondary goals being speed, ease of use, and interpretability of the resulting predictive model. While pattern classification has shown promise in many areas of practical significance, it faces difficult challenges posed by real world problems, of which the most pronounced is Bellman’s curse of dimensionality: it states the fact that the sample size required to perform accurate prediction on problems with high dimensionality is beyond feasibility. This is because in high dimensional spaces data become extremely sparse and are apart from each other. As a result, severe bias that affects any estimation process can be introduced in a high dimensional feature space with finite samples. Learning tasks with data represented as a collection of a very large number of features abound. For example, microarrays contain an overwhelming number of genes relative to the number of samples. The Internet is a vast repository of disparate information growing at an exponential rate. Efficient and effective document retrieval and classification systems are required to turn the ocean of bits around us into useful information, and eventually into knowledge. This is a challenging task, since a word level representation of documents easily leads 30000 or more dimensions. This chapter discusses classification techniques to mitigate the curse of dimensionality and reduce bias, by estimating feature relevance and selecting features accordingly. This issue has both theoretical and practical relevance, since many applications can benefit from improvement in prediction performance.

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