Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel technique—Floating Centroids Method (FCM) designed to improve the performance of a conventional neural network classifier. Partition space is a space that is used to categorize data sample after sample is mapped by neural network. In the partition space, the centroid is a point, which denotes the center of a class. In a conventional neural network classifier, position of centroids and the relationship beten centroids and classes are set manually. In addition, number of centroids is fixed with reference to the number of classes. The proposed approach introduces many floating centroids, which are spread throughout the partition space and obtained by using K-Means algorithm. Moreover, different classes labels are attached to these centroids automatically. A sample is predicted as a certain class if the closest centroid of its corresponding mapped point is labeled by this class. Experimental results illustrate that the proposed method has favorable performance especially with respect to the training accuracy, generalization accuracy, and average F-measures.
['Lin Wang', 'Bo Yang', 'Yuehui Chen', 'Ajith Abraham', 'Hongwei Sun', 'Zhenxiang Chen', 'Haiyang Wang']
I am writing a research paper on a idea Improvement of neural network classifier using floating centroids
[{"from": "human", "value": "I am writing a research paper on a idea Improvement of neural network classifier using floating centroids"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel technique\u2014Floating Centroids Method (FCM) designed to improve the performance of a conventional neural network classifier. Partition space is a space that is used to categorize data sample after sample is mapped by neural network. In the partition space, the centroid is a point, which denotes the center of a class. In a conventional neural network classifier, position of centroids and the relationship beten centroids and classes are set manually. In addition, number of centroids is fixed with reference to the number of classes. The proposed approach introduces many floating centroids, which are spread throughout the partition space and obtained by using K-Means algorithm. Moreover, different classes labels are attached to these centroids automatically. A sample is predicted as a certain class if the closest centroid of its corresponding mapped point is labeled by this class. Experimental results illustrate that the proposed method has favorable performance especially with respect to the training accuracy, generalization accuracy, and average F-measures."}]