Synergetic research response classifiers for multiple domains
نویسندگان
چکیده
منابع مشابه
Accurate Probability Calibration for Multiple Classifiers
In classification problems, isotonic regression has been commonly used to map the prediction scores to posterior class probabilities. However, isotonic regression may suffer from overfitting, and the learned mapping is often discontinuous. Besides, current efforts mainly focus on the calibration of a single classifier. As different classifiers have different strengths, a combination of them can...
متن کاملMultiple Classifiers for Electronic Nose Data
In this contribution we apply a method -called boostingfor constructing a classifier out of a set of (base or weak) classifiers for the discrimination of two groups of coffees (blends and monovarieties). The main idea of boosting is to produce a sequence of base classifiers that progressively concentrate on the hard patterns, i.e. those which are near to the classification boundary. Measurement...
متن کاملCombining multiple classifiers for wrapper feature selection
Wrapper feature selection methods are widely used to select relevant features. However, wrappers only use a single classifier. The downside to this approach is that each classifier will have its own biases and will therefore select very different features. In order to overcome the biases of individual classifiers, this study introduces a new data mining method called wrapper-based decision tree...
متن کاملConfusion Matrix Disagreement for Multiple Classifiers
We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the disagreement concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a Distance-based Disagreement (DbD) measure using an Euclidean distance computed between confusion matrices ...
متن کاملCascaded multiple classifiers for secondary structure prediction.
We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and J.A. Cuff). This...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Engineering & Technology
سال: 2018
ISSN: 2227-524X
DOI: 10.14419/ijet.v7i2.21.12440