Abstract: Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results.
Abstract: Artificial Immune Systems (AIS), inspired by the
human immune system, are algorithms and mechanisms which are
self-adaptive and self-learning classifiers capable of recognizing and
classifying by learning, long-term memory and association. Unlike
other human system inspired techniques like genetic algorithms and
neural networks, AIS includes a range of algorithms modeling on
different immune mechanism of the body. In this paper, a mechanism
of a human immune system based on apoptosis is adopted to build an
Intrusion Detection System (IDS) to protect computer networks.
Features are selected from network traffic using Fisher Score. Based
on the selected features, the record/connection is classified as either
an attack or normal traffic by the proposed methodology. Simulation
results demonstrates that the proposed AIS based on apoptosis
performs better than existing AIS for intrusion detection.