6776829
Semi-supervised Kernel-Based Fuzzy C-Means
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S 2 KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algo- rithm. Through using labeled and unlabeled data together, S 2 KFCM can be ap- plied to both clustering and classification tasks. However, only the latter is con- cerned in this paper. Experimental results show that S 2 KFCM can improve classification accuracy significantly, compared with conventional classifiers trained with a small number of labeled data only. Also, it outperforms a similar approach S 2 FCM.
120730704
Robust image denoising using kernel-induced measures
We propose a class of novel nonlinear robust filters for image denoising by incorporating the kernel-induced measures into the classical linear mean filter. Particularly, we place more focus on the Gaussian kernel based filter (GK) due to its simplicity. The GK filter not only generalizes and makes the original linear mean filter highly resistant to outliers but also outperforms a typical and powerful mean-logCauchy filter recently developed by Hamza et al in the mixed noise removal in certain specific conditions in the normalized mean square error (NMSE) sense. The experimental results also illustrate that the kernel-based nonlinear filters are promising.
12986050
Improving the Robustness of ‘Online Agglomerative Clustering Method’ Based on Kernel-Induce Distance Measures
Recently, an online agglomerative clustering method called AddC (I. D. Guedalia et al. Neural Comput. {\bf 11} (1999), 521--540) was proposed for nonstationary data clustering. Although AddC possesses many good attributes, a vital problem of that method is its sensitivity to noises, which limits its use in real-word applications. In this paper, based on \hbox{kernel-induced} distance measures, a robust online clustering (ROC) algorithm is proposed to remedy the problem of AddC. Experimental results on artificial and benchmark data sets show that ROC has better clustering performances than AddC, while still inheriting advantages such as clustering data in a single pass and without knowing the exact number of clusters beforehand.
5733190
Automating the drug scheduling of cancer chemotherapy via evolutionary computation
This paper presents the optimal control of drug scheduling in cancer chemotherapy using a distributed evolutionary computing software. Unlike conventional methods that often require gradient information or hybridization of different approaches in drug scheduling, the proposed evolutionary optimization methodology is simple and capable of automatically finding the near-optimal solutions for complex cancer chemotherapy problems. It is shown that different number of variable pairs in evolutionary representation for drug scheduling can be easily implemented via the software, since the computational workload is shared and distributed among multiple computers over the Internet. Simulation results show that the proposed evolutionary approach produces excellent control of drug scheduling in cancer chemotherapy, which are competitive or equivalent to the best solutions published in literature.
39015345
FPGA-BASED AUTONOMOUS ROBOT NAVIGATION VIA INTRINSIC EVOLUTION
Artificial evolution has been applied in mobile robotics to achieve adaptive robotic behaviors in unstructured environments. Meanwhile, much attention has been paid to the evolvable hardware, which is a novel set of integrated circuits capable of reconfiguring its architectures using evolutionary computing techniques. This paper presents the design and implementation of an evolvable hardware-based autonomous robot navigation system using intrinsic evolution. Distinguished from traditional evolutionary approaches based on software simulation, an evolvable robot controller at the hardware gate-level that is capable of adapting to dynamic changes in the environments is implemented. In our approach, the concept of Boolean function is used to construct the evolvable controller implemented on an FPGA-based robot turret, and evolutionary computing is applied as a learning tool to guide the artificial evolution at the hardware level. The effectiveness of the proposed evolvable robotic system is confirmed with physical implementation of robot navigation behaviors on light source following and obstacle avoidance using a robot with traction fault.
12902312
Evolutionary computing for knowledge discovery in medical diagnosis
One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is utilized to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimize the numeric attributes for concise classification rules without the need of discretization. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.
11570061
Graphical Composition of Grid Services
Grid services and web services have similarities but also significant differences. Although conceived for web services, it is seen how BPEL (Business Process Execution Logic) can be used to orchestrate a collection of grid services. It is explained how CRESS (Chisel Representation Employing Systematic Specification) has been extended to describe grid service composition. The CRESS descriptions are automatically converted into BPEL/WSDL code for practical realisation of the composed services. This achieves orchestration of grid services deployed using the widely used Globus Toolkit and ActiveBPEL interpreter. The same CRESS descriptions are automatically translated into LOTOS, allowing systematic checks for interoperability and logical errors prior to implementation.
37111452
Heuristics for Vehicle Routing Problem with Time Windows