malizo machacha | Botho College (original) (raw)
Uploads
Papers by malizo machacha
The development of microarray technology has supplied a large volume of data to many fields. This... more The development of microarray technology has supplied a large volume of data to many fields. This data has thousands of features and is also very noisy. So it is very difficult to represent and understand its complex relationships directly. In this paper we propose a method, called case-based reasoning classifier( CBR) that improves the performance considerably when applied to cancer classification from gene expression data. We also used the Mahalanobis classifier which accurately classifies the gene expression data. In our analysis, a benchmark dataset such as Leukemia cancer dataset, have been used. Experimental results indicate that the above classifier produces a better recognition rate on the benchmark dataset.
When making decisions we need to consider the possible alternatives and then choose the optimal a... more When making decisions we need to consider the possible alternatives and then choose the optimal alternative. The uncertainty of subjective judgment is present during this selection process. Also, decision making becomes difficult when the available information is incomplete or imprecise. This kind of problem exists while selecting a project. There are also several critical factors that are involved in the selection process, including market conditions, availability of raw materials, etc. The decision mechanism is constrained by the uncertainty inherent in the determination of the relative importance of each attribute element. In this paper, we develop a system for the project selection using fuzzy logic. Fuzzy logic enables us to emulate the human reasoning process and make decisions based on vague or imprecise data. Our approach is based on uncertainty reduction. The optimal alternative is formed by the relative weights of each attribute's elements combined over all the attribute membership functions. We also do a case study for the selection of software packages. Our system could be easily applied to other project selection problems under uncertainty.
The development of microarray technology has supplied a large volume of data to many fields. This... more The development of microarray technology has supplied a large volume of data to many fields. This data has thousands of features and is also very noisy. So it is very difficult to represent and understand its complex relationships directly. In this paper we propose a method, called case-based reasoning classifier( CBR) that improves the performance considerably when applied to cancer classification from gene expression data. We also used the Mahalanobis classifier which accurately classifies the gene expression data. In our analysis, a benchmark dataset such as Leukemia cancer dataset, have been used. Experimental results indicate that the above classifier produces a better recognition rate on the benchmark dataset.
When making decisions we need to consider the possible alternatives and then choose the optimal a... more When making decisions we need to consider the possible alternatives and then choose the optimal alternative. The uncertainty of subjective judgment is present during this selection process. Also, decision making becomes difficult when the available information is incomplete or imprecise. This kind of problem exists while selecting a project. There are also several critical factors that are involved in the selection process, including market conditions, availability of raw materials, etc. The decision mechanism is constrained by the uncertainty inherent in the determination of the relative importance of each attribute element. In this paper, we develop a system for the project selection using fuzzy logic. Fuzzy logic enables us to emulate the human reasoning process and make decisions based on vague or imprecise data. Our approach is based on uncertainty reduction. The optimal alternative is formed by the relative weights of each attribute's elements combined over all the attribute membership functions. We also do a case study for the selection of software packages. Our system could be easily applied to other project selection problems under uncertainty.