Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mutual information (MI) is a porful concept for correlation-centric applications. It has been used for feature selection from microarray gene expression data in many works. One of the merits of MI is that, unlike many other heuristic methods, it is based on a mature theoretic foundation. When applied to microarray data, hover, it faces some challenges. First, due to the large number of features (i.e., genes) present in microarray data, the true distributions for the expression values of some genes may be distorted by noise. Second, evaluating inter-group mutual information requires estimating multi-variate distributions, which is quite difficult if not impossible. To address these problems, in this paper,  propose a new MI-based feature selection approach for microarray data. Our approach relies on two strategies: one is  relevance boosting , which requires a desirable feature to show substantially additional relevance with class labeling beyond the already selected features, the other is   feature interaction enhancing , which probabilistically compensates for feature interaction missing from simple aggregation-based evaluation.  justify our approach from both theoretical perspective and experimental results.  use a synthetic dataset to show the statistical significance of the proposed strategies, and real-life datasets to show the improved performance of our approach over the existing methods.
['Jian Tang', 'Shuigeng Zhou']
I am writing a research paper on a idea A New Approach for Feature Selection from Microarray Data Based on Mutual Information
[{"from": "human", "value": "I am writing a research paper on a idea A New Approach for Feature Selection from Microarray Data Based on Mutual Information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mutual information (MI) is a porful concept for correlation-centric applications. It has been used for feature selection from microarray gene expression data in many works. One of the merits of MI is that, unlike many other heuristic methods, it is based on a mature theoretic foundation. When applied to microarray data, hover, it faces some challenges. First, due to the large number of features (i.e., genes) present in microarray data, the true distributions for the expression values of some genes may be distorted by noise. Second, evaluating inter-group mutual information requires estimating multi-variate distributions, which is quite difficult if not impossible. To address these problems, in this paper,  propose a new MI-based feature selection approach for microarray data. Our approach relies on two strategies: one is  relevance boosting , which requires a desirable feature to show substantially additional relevance with class labeling beyond the already selected features, the other is   feature interaction enhancing , which probabilistically compensates for feature interaction missing from simple aggregation-based evaluation.  justify our approach from both theoretical perspective and experimental results.  use a synthetic dataset to show the statistical significance of the proposed strategies, and real-life datasets to show the improved performance of our approach over the existing methods."}]