Feature subset selection problem on microarray data

Recent advance of technology gave birth to tools such as microarray chips. The use of microarray chips enabled the scientists to measure the amount of protein production from their genes in a cell, known as the gene expression data. The classification of cell samples by means of their gene expression data is a hot research area. The data used for the analysis is massive and therefore the features, i.e., the genes, must be reduced to a reasonable level due to the computational cost of experiments and the possibility of misleading irrelevant genes. Therefore, usually, the analysis based on the classification of cell samples includes a feature subset selection phase. This thesis aims to develop a tool that can be used during the feature subset selection phase of such analyses. Three novel algorithms are proposed for the gene selection problem based on basic association rule mining. The first algorithm starts with fuzzy partitioning of the gene expression data and discovers highly confident IF-THEN rules that enable the classification of sample tissues. The second algorithm search the possible IFTHEN rules based on a heuristic pruning approach which is based on the beam search algorithm. Finally, the third algorithm focuses on the hierarchical information carried through gene expressions by constructing decision trees based on different performance measures. We found satisfactory results in Leukemia Dataset. In addition, in colon cancer dataset, algorithm that is based on construction of decision trees showed good performance

A taxonomy of logistics innovations

In this paper we present a taxonomy of supply chain and logistics innovations, which is based on an extensive literature survey. Our primary goal is to provide guidelines for choosing the most appropriate innovations for a company, such that the company can outrun its competitors. We investigate the factors, both internal and external to the company, that determine the applicability and effectiveness of the listed innovations. We support our suggestions with real world cases reported in literature

A taxonomy of supply chain innovations

In this paper, a taxonomy of supply chain and logistics innovations was developed and presented. The taxonomy was based on an extensive literature survey of both theoretical research and case studies. The primary goals are to provide guidelines for choosing the most appropriate innovations for a company, and help companies in positioning themselves in the supply of chain innovations landscape. To this end, the three dimensions of supply chain innovations, namely the goals, supply chain attributes, and innovation attributes were identified and classified. The taxonomy allows for the efficient representation of critical supply chain innovations information, and serves the mentioned goals, which are fundamental to companies in a multitude of industries