No abstract

Given the economic squeeze world over, search for what we call frugal grassroots innovations in Honey Bee Network, has become even more urgent and relevant in the recent years. And, to shape this search, models and concepts like open innovation, reverse innovation (GE, Market-Relevant Design: Making ECGs Available

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. The classification of tumors was and is both a practical and theoretic necessity and requirement. DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using Nonnegative Matrix Factorization (NMF). In order to improve the performance of classification, Symmetry NMF (SymNMF) is used in this approach. Then, features are extracted from the selected genes by virtue SymNMF. As a last step, an efficient machine learning approach is used to classify the tumor samples using the extracted features. In order for a better classification, Support Vector Machine with Weighted Kernel Width (WSVM) is used in this classification approach. The performance of the proposed approach is tested using colon cancer data set and the acute leukemia data set. It is observed from the experimental results that the proposed approach provides better performance when compared with the traditional approaches.

Recent research shows that rule based models perform well while classifying large data sets such as data streams with concept drifts. A genetic algorithm is a strong rule based classification algorithm which is used only for mining static small data sets. If the genetic algorithm can be made scalable and adaptable by reducing its I/O intensity, it will become an efficient and effective tool for mining large data sets like data streams. In this paper a scalable and adaptable online genetic algorithm is proposed to mine classification rules for the data streams with concept drifts. Since the data streams are generated continuously in a rapid rate, the proposed method does not use a fixed static data set for fitness calculation. Instead, it extracts a small snapshot of the training example from the current part of data stream whenever data is required for the fitness calculation. The proposed method also builds rules for all the classes separately in a parallel independent iterative manner. This makes the proposed method scalable to the data streams and also adaptable to the concept drifts that occur in the data stream in a fast and more natural way without storing the whole stream or a part of the stream in a compressed form as done by the other rule based algorithms. The results of the proposed method are comparable with the other standard methods which are used for mining the data streams.

No abstract