45862220
Ensembles of Multi-Instance Neural Networks
Recently, multi-instance classification algorithm BP-MIP and multiinstance regression algorithm BP-MIR both based on neural networks have been proposed. In this paper, neural network ensemble techniques are introduced to solve multi-instance learning problems, where BP-MIP ensemble and BP-MIR ensemble are constructed respectively. Experiments on benchmark and artificial data sets show that ensembles of multi-instance neural networks are superior to single multi-instance neural networks in solving multiinstance problems.
7567561
Genetic Algorithm based Selective Neural Network Ensemble
Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual networks in some cases. Therefore an approach named GASEN is proposed, which trains several individual neural networks and then employs genetic algorithm to select an optimum subset of individual networks to constitute an ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e. averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost.
16987945
Mining typhoon knowledge with neural networks
Neural network technology has not been fully utilised in data mining. There are two reasons for this. Firstly, most neural algorithms need long-term training, and cannot perform incremental learning. Secondly, the knowledge learned by neural networks is concealed within a large quantity of connections. We have develop a neural network method to mine typhoon knowledge which overcomes those two obstacles. Experimental results show that our method shows considerable promise.
8877735
Pose invariant face recognition
We describe a novel neural network architecture, which can recognize human faces with any view in a certain viewing angle range (from left 30 degrees to right 30 degrees out of plane rotation). View-specific eigenface analysis is used as the front-end of the system to extract features, and the neural network ensemble is used for recognition. Experimental results show that the recognition accuracy of our network ensemble is higher than conventional methods such as using a single neural network to recognize faces of a specific view.
10964754
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the self-training process may erroneously label some unlabeled examples, sometimes the learned hypothesis does not perform well. In this paper, a new algorithm named Setred is proposed, which utilizes a specific data editing method to identify and remove the mislabeled examples from the self-labeled data. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that the introduction of data editing is beneficial, and the learned hypotheses of Setred outperform those learned by the standard self-training algorithm.
31771496
Spiculated Lesion Detection in Digital Mammogram Based on Artificial Neural Network Ensemble
Among breast abnormalities, spiculated lesions are one of the most difficult type of tumor to detect. In this paper, we apply a feature extraction method to generate four feature images for a single mammogram, and then partition every feature image into a series of small square blocks. The four average feature values of each block are considered as an instance describing the block. Finally we use an artificial neural network ensemble method to detect the spiculated lesions. Experiments show that the accuracy of this method is well on digital mammograms.
89476
Artificial intelligence in medicine in China
In recent years, scientists in China have made significant contributions in many fields of artificial intelligence, among which a very important field is Artificial Intelligence in Medicine (AIM). The aim of this special issue is to help international colleagues have a better understanding of the current research activities in this field carried out in China. Traditional Chinese Medicine (TCM) is one of the most illustrious contributions of Chinese to the world. But since TCM appeared before the naissance of modern sciences, most of its principles and working mechanisms are very difficult to be analyzed and understood by modern scientific brains. In this special issue, there are two papers devoted to the processing of TCM knowledge with ontology technology. In the first paper, C. Cao, H. Wang and Y. Sui present an ontology for herbal drugs and formulae of TCM. The ontology is frame-oriented, which consists of a set of classes and their relations, and formal axioms for constraining the interpretation of these classes and relations. Based on the ontology, they design an executable knowledge extraction language and develop a system for extracting knowledge of drugs and formulae from semi-structured text. This work indicates that a domain-specific ontology could have a significant impact on knowledge acquisition from text. Various kinds of TCM terminology systems with different purposes have been developed in China. An ontological approach to the description of terminology systems will allow a better integration and reuse of these systems. The paper of X. Zhou, Z. Wu, A. Yin, L. Wu, W. Fan and R. Zhang describes the Unified Traditional Chinese Medical Language System (UTCMLS) project, which involves a broad collaboration of 16 research groups aiming at supporting TCM language knowledge storage, concept-based information retrieval and information integration. It is noteworthy that the progress of this project may help standardize TCM terminology. The following two papers focus on medical image processing. Fractal dimension is usually regarded as a global image feature in previous research, while X. Zhuang and Q. Meng suggest using local fuzzy fractal dimension (LFFD) as a new local feature of medical images. The estimation of LFFD is realized by an extension of the pixel-covering method, which is often used in estimating the box-counting dimension for 2D monochrome images, incorporated with the fuzzy set. Their experiments show that LFFD is useful for edge detection in medical images, and can also provide helpful information for the segmentation of echocardiogram image sequences. In D.-Q. Zhang and S.-C. Chen’s paper, a novel algorithm for fuzzy segmentation of MRI images is proposed. At first, the kernelized fuzzy C-means (KFCM) algorithm is developed through replacing the Euclidean distance in the conventional fuzzy C-means algorithm by a kernel-induced distance metric. Then, a spatial penalty is added to the objective function of KFCM. Their experiments show that the proposed algorithm obtains good performance when the MRI images are corrupted by noise or other artifacts. A proof of the robustness of KFCM is also provided in the appendix of this paper. The final paper in this special issue is on virtual reality-based medical applications. Through considering deformation microscopically as a process of force transmittal among the constituting mass points, K.-S. Choi, H. Sun and P.A. Heng present an efficient and scalable deformable model whose parameters are optimized with simulated annealing using the reference data generated with the linear static finite element method. This model has been applied to simulate the typical behavior of living tissues and anisotropic materials. Integration with a haptic device has also been achieved on a personal computer platform. It is worth noting that although we have tried our best to enable this special issue reflect the research activities on AIM in China, it does not mean that this issue presents all the recent progresses. This is Artificial Intelligence in Medicine (2004) 32, 1—2
8244593
A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm
This paper presents a study on hierarchical surrogate-assisted evolutionary algorithm (HSAEA) using different global surrogate models for solving computationally expensive optimization problems. In particular, we consider the use of Gaussian process (GP) and polynomial regression (PR) methods for approximating the global fitness landscape in the surrogate-assisted evolutionary search. The global surrogate model serves to pre-screen the EA population for promising individuals. Subsequently, these potential individuals undergo a local search in the form of Lamarckian learning using online local surrogate models. Numerical results are presented on two multimodal benchmark test functions. The results obtained show that both PR-HSAEA and GP-HSAEA converge to good designs on a limited computational budget. Further, our study also shows that the GP model is suitable for global surrogate modeling in HSAEA if the evaluation function is very expensive in computations. On moderately expensive problems, the PR method may serve to generate better efficiency than using GP.
205343152
On queuing lengths in on-line switching
Queues that temporarily store fixed-length packets are ubiquitous in network switches. Scheduling algorithms that prevent packet-loss are always desirable. LONGEST-QUEUE-FIRST (LQF) is an on-line greedy algorithm widely exploited because of its simplicity and efficiency. In this paper, we give improved bounds on the competitive ratio of LQF in terms of the worst-case queuing length, parameterized with respect to the optimal queuing length of a clairvoyant adversary. This gives a better picture of LQF's performance under heavy traffic than the usual (unparameterized) competitive ratio. We also discuss randomization, and we conclude with some intriguing open problems regarding a two-dimensional generalization of the problem.