Biomedical subjects

B John Oommen

Publications and source records attributed to B John Oommen.

4 recordsLinked to original sources

PubMed2006

On optimizing syntactic pattern recognition using tries and AI-based heuristic-search strategies.

This paper deals with the problem of estimating, using enhanced artificial-intelligence (AI) techniques, a transmitted string X* by processing the corresponding string Y, which is a noisy version of X*. It is assumed that Y contains substitution, insertion, and deletion (SID) errors. The best estimate X+ of X* is defined as that element of a dictionary H that minimizes the generalized Levenshtein distance (GLD) D (X, Y) between X and Y, for all X epsilon H. In this paper, it is shown how to evaluate D (X, Y) for every X epsilon H simultaneously, when the edit distances are general and the maximum number of errors is not given a priori, and when H is stored as a trie. A new scheme called clustered beam search (CBS) is first introduced, which is a heuristic-based search approach that enhances the well-known beam-search (BS) techniques used in AI. The new scheme is then applied to the approximate string-matching problem when the dictionary is stored as a trie. The new technique is compared with the benchmark depth-first search (DFS) trie-based technique (with respect to time and accuracy) using large and small dictionaries. The results demonstrate a marked improvement of up to 75% with respect to the total number of operations needed on three benchmark dictionaries, while yielding an accuracy comparable to the optimal. Experiments are also done to show the benefits of the CBS over the BS when the search is done on the trie. The results also demonstrate a marked improvement (more than 91%) for large dictionaries.

Algorithms↗

PubMed2005

On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers.

In Kernel-based Nonlinear Subspace (KNS) methods, the subspace dimensions have a strong influence on the performance of the subspace classifier. In order to get a high classification accuracy, a large dimension is generally required. However, if the chosen subspace dimension is too large, it leads to a low performance due to the overlapping of the resultant subspaces and, if it is too small, it increases the classification error due to the poor resulting approximation. The most common approach is of an ad hoc nature, which selects the dimensions based on the so-called cumulative proportion computed from the kernel matrix for each class. In this paper, we propose a new method of systematically and efficiently selecting optimal or near-optimal subspace dimensions for KNS classifiers using a search strategy and a heuristic function termed the Overlapping criterion. The rationale for this function has been motivated in the body of the paper. The task of selecting optimal subspace dimensions is reduced to finding the best ones from a given problem-domain solution space using this criterion as a heuristic function. Thus, the search space can be pruned to very efficiently find the best solution. Our experimental results demonstrate that the proposed mechanism selects the dimensions efficiently without sacrificing the classification accuracy.

Algorithms↗

PubMed2005

Dynamic algorithms for the shortest path routing problem: learning automata-based solutions.

This paper presents the first Learning Automaton-based solution to the dynamic single source shortest path problem. It involves finding the shortest path in a single-source stochastic graph topology where there are continuous probabilistic updates in the edge-weights. The algorithm is significantly more efficient than the existing solutions, and can be used to find the "statistical" shortest path tree in the "average" graph topology. It converges to this solution irrespective of whether there are new changes in edge-weights taking place or not. In such random settings, the proposed learning automata solution converges to the set of shortest paths. On the other hand, the existing algorithms will fail to exhibit such a behavior, and would recalculate the affected shortest paths after each weight-change. The important contribution of the proposed algorithm is that all the edges in a stochastic graph are not probed, and even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the shortest path graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. All the algorithms were tested in environments where edge-weights change stochastically, and where the graph topologies undergo multiple simultaneous edge-weight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. The algorithm can be applicable in domains ranging from ground transportation to aerospace, from civilian applications to military, from spatial database applications to telecommunications networking.

Algorithms↗

PubMed2004

Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets.

Most of the prototype reduction schemes (PRS), which have been reported in the literature, process the data in its entirety to yield a subset of prototypes that are useful in nearest-neighbor-like classification. Foremost among these are the prototypes for nearest neighbor classifiers, the vector quantization technique, and the support vector machines. These methods suffer from a major disadvantage, namely, that of the excessive computational burden encountered by processing all the data. In this paper, we suggest a recursive and computationally superior mechanism referred to as adaptive recursive partitioning (ARP)_PRS. Rather than process all the data using a PRS, we propose that the data be recursively subdivided into smaller subsets. This recursive subdivision can be arbitrary, and need not utilize any underlying clustering philosophy. The advantage of ARP_PRS is that the PRS processes subsets of data points that effectively sample the entire space to yield smaller subsets of prototypes. These prototypes are then, in turn, gathered and processed by the PRS to yield more refined prototypes. In this manner, prototypes which are in the interior of the Voronoi spaces, and thus ineffective in the classification, are eliminated at the subsequent invocations of the PRS. We are unaware of any PRS that employs such a recursive philosophy. Although we marginally forfeit accuracy in return for computational efficiency, our experimental results demonstrate that the proposed recursive mechanism yields classification comparable to the best reported prototype condensation schemes reported to-date. Indeed, this is true for both artificial data sets and for samples involving real-life data sets. The results especially demonstrate that a fair computational advantage can be obtained by using such a recursive strategy for "large" data sets, such as those involved in data mining and text categorization applications.

Algorithms↗