Breast Cancer Detection with Reduced Feature Set

Breast Cancer Detection with Reduced Feature Set

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such a...

Online Inference for Relation Extraction with a Reduced Feature Set

Access to web-scale corpora is gradually bringing robust automatic knowledge base creation and extension within reach. To exploit these large unannotated—and extremely difficult to annotate—corpora, unsupervised machine learning methods are required. Probabilistic models of text have recently found some success as such a tool, but scalability remains an obstacle in their application, with stand...

Comparison of Feature Extraction Methods for Breast Cancer Detection

Face Detection Using Neural Learning with Optimized Feature Set

A system using feature optimization and neural learning techniques is presented for detection of upright frontal faces. The design of the system takes advantages of the existing techniques and avoid their shortcomings. Statistical pattern recognition (PR) techniques are used to optimize the feature selection. A neural network (NN) method is used to learn a complex mapping function for the class...

Intrusion Detection using Supervised Learning with Feature Set Reduction

Intrusion detection systems intend to recognize attacks with a low false positive rate and high detection rate. Many feature selection methods introduced to eliminate redundant and irrelevant features, because raw features may abbreviate accuracy or robustness of classification. In this paper we are proposing the information gain technique for the selection of the features. A feature with the h...