A software package developed for the purpose of feature selection in statistical pattern recognition is presented. The software tool includes both several classical and new methods suitable for dimensionality reduction, classi.cation and data representation. Examples of solved problems are given, as well as observations regarding the behavior of criterion functions. ? 2002 Pattern Recognition S...

malay home surroundings is varies in plants pattern as well as a variety of size. the plants speciescultivated own their particular reasons. malay house environment is so harmony with humans, mother nature alsoculture relationship has visualise a unique environmental design. in this accomplishing research, it will provide anunderstanding of plants adoption of malay house environment. environmen...

A comparative study of algorithms for large-scale feature selection (where the number of features is over 50) is carried out. In the study, the goodness of a feature subset is measured by leave-one-out correct-classi"cation rate of a nearestneighbor (1-NN) classi"er and many practical problems are used. A uni"ed way is given to compare algorithms having dissimilar objectives. Based on the resul...

Selecting an optimal subset from original large feature set in the design of pattern classi"er is an important and di$cult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and boundmethod, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential...

In this thesis, the uses of Artificial Immune Systems (AIS) in Machine learning is studded. the thesis focus on some of immune inspired algorithms such as clonal selection algorithm and artificial immune network. The effect of changing the algorithm parameter on its performance is studded. Then a new immune inspired algorithm for unsupervised classification is proposed. The new algorithm is bas...

Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; ...

a method based on electrical capacitance tomography (ect) and an improved least squares support vector machine (ls-svm) is proposed for void fraction measurement of oil-gas two-phase flow. in the modeling stage, to solve the two problems in ls-svm, pruning skills are employed to make ls-svm sparse and robust; then the real-coded genetic algorithm is introduced to solve the difficult problem of ...

Feature selection researchers often encounter a peaking phenomenon: a feature subset can be found that is smaller but still enables building a more accurate classifier than the full set of all the candidate features. However, the present study shows that this peak may often be just an artifact due to the still too common mistake in pattern recognition — that of not using an independent test set.

The following acquisition/selection problem is considered: a group of N targets is observed at time t0 and one of them is designated (targeting information). At some later time t1 the target group is again observed from a stand-oo missile. The targets are assumed to move as a group, as well as individually, between observation times and so have a dependent motion model. The detection probabilit...