Discretization can turn numeric attributes into discrete ones. Feature selection can eliminate some irrelevant and/or redundant attributes. Chi2 is a simple and general algorithm that uses the /spl chi//sup 2/ statistic to discretize numeric attributes repeatedly until some inconsistencies are found in the data. It achieves feature selection via discretization. It can handle mixed attributes, work with multiclass data, and remove irrelevant and redundant attributes.

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Feature selection via discretization

New approaches for estimating global solar radiation across Sudan

Oxygen consumption and protein synthesis were measured concurently in four fish cell types: BF-2 and TRG-2 cell lines, rainbow trout macrophages and scale cells. The fractional rates of protein synthesis (percentage of the protein mass synthesised per day) were ranked: BF-2 cells > macrophages > RTG-2 cells > scale cells. Oxygen consumption rates were ranked BF-2 cells = macrophages = RTG-2 cells > scale cells. Within three of the cell types (BF-2, RTG-2 and scale cells) oxygen consumption and protein synthesis were linearly correlated, whereas comparison between the four cell types gave rise to an exponential relationship between fractional rates of protein synthesis and oxygen consumption. Inhibition of protein synthesis with cycloheximide by 41–65% resulted in a 62–89% reduction in oxygen consumption depending on cell type. Calculations of the aerobic cost of protein synthesis using the cycloheximide-sensitive protein synthesis and oxygen consumption rates resulted in estimates ranging from 11 to 217 μmol O2·mg protein-1 synthesised depending on the cell type. The lowest net protein synthesis costs, which are close to theoretical values for peptide bond formation, were associated with the most rapid rates of protein synthesis.

Protein synthesis and oxygen consumption in fish cells

No matter how great a tool is, if it is applied to a wrong problem, it may delay problem solving, or even worse, cause harm. Experts are needed to match tools with problems. They are not only just domain experts who know a lot about the problems they are facing, but experts who are familiar with feature selection methods. If it is not impossible for us to have such experts, these experts are rare to find. The matching problem still exists regardless of whether we can find such experts or not. The second best solution is to abstract both tools and problems. By relating tools with problems, hopefully, we can help solve the matching problem. Abstracting problems can be done by domain experts according to characteristics of data; abstracting tools requires some measures that can tell us how good each method is and under what circumstances it is good. In order to wisely apply feature selection methods, we need to first discuss their performance.

S. A. Baines

Papers

Statistical Tables for Science, Engineering, Management and Business Studies