Computing and Information Systems - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 43
-
ItemA voting approach to identify a small number of highly predictive genes using multiple classifiers(BMC, 2009-01-30)BACKGROUND: Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage. RESULTS: By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer. CONCLUSION: We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.
-
ItemA Weighting Scheme Based on Emerging Patterns for Weighted Support Vector Machines( 2005)Support Vector Machines (SVMs) are powerful tools for solving classification problems and have been applied to many application fields, such as pattern recognition and data mining, in the past decade. Weighted Support Vector Machines (weighted SVMs) extend SVMs by considering that different input vectors make different contributions to the learning of decision surface. An important issue in training weighted SVMs is how to develop a reliable weighting model to reflect the true noise distribution in the training data, i.e., noise and outliers should have low weights. In this paper we propose to use Emerging Patterns (EPs) to construct such a model. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other class. Since EPs of a given class represent the discriminating knowledge unique to their home class, noise and outliers should contain no EPs or EPs of the both contradicting classes, while a representative instance of the class should contain strong EPs of the same class. We calculate numeric scores for each instance based on EPs, and then assign weights to the training data using those scores. An extensive experiments carried out on a large number of benchmark datasets show that our weighting scheme often improves the performance of weighted SVMs over SVMs. We argue that the improvement is due to the ability of our model to approximate the true distribution of data points.
-
ItemAnalysis and Enhancement of On-demand Routing in Wireless Sensor Networks(ASSOC COMPUTING MACHINERY, 2008)
-
ItemARTS: Agent-oriented robust transactional system(ACM, 2007-12-01)
-
ItemExpanding the Training Data Space Using Emerging Patterns and Genetic Methods(Society for Industrial and Applied Mathematics, 2005)
-
ItemProactive Traffic Merging Strategies for Sensor-Enabled Cars(ASSOC COMPUTING MACHINERY, 2007)
-
Item
-
ItemProactive Traffic Merging Strategies for Sensor-Enabled Cars(IGI Global, 2009)Congestion is a major challenge in today’s road traffic. The primary cause is bottlenecks such as ramps leading onto highways, or lane blockage due to obstacles. In these situations, the road capacity reduces because several traffic streams merge to fewer streams. Another important factor is the non-coordinated driving behavior resulting from the lack of information or the intention to minimize the travel time of a single car. This chapter surveys traffic control strategies for optimizing traffic flow on highways, with a focus on more adaptive and flexible strategies facilitated by current advancements in sensor-enabled cars and vehicular ad hoc networks (VANETs). The authors investigate proactive merging strategies assuming that sensor-enabled cars can detect the distance to neighboring cars and communicate their velocity and acceleration among each other. Proactive merging strategies can significantly improve traffic flow by increasing it up to 100% and reduce the overall travel delay by 30%.
-
ItemRoleVAT: Visual Assessment of Practical Need for Role Based Access Control(IEEE COMPUTER SOC, 2009)
-
Item
- 1 (current)
- 2
- 3
- 4
- 5