Enhanced probabilistic neural network with local decision circles: A robust classifier
Enhanced probabilistic neural network with local decision circles: A robust classifier
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DOI：
10.3233/ica-2010-0345
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发表时间：
2010-01-01
影响因子：
6.5
通讯作者：
Adeli, Hojjat
中科院分区：
文献类型：
--
作者：
Ahmadlou, Mehran;Adeli, Hojjat
近年来，概率神经网络（PPN）以其简单、高效的特点得到了广泛的应用。PNN将测试数据分配给与其他类相比具有最大似然的类。使用简单的贝叶斯规则，通过核密度估计，在模式层计算测试数据与每个训练数据的似然。核函数通常是一个标准的概率分布函数，比如高斯函数。扩展参数用作全局参数，它决定内核的宽度。模式层中的贝叶斯规则在给定输入向量的情况下估计每个类的条件概率，而不考虑训练数据中任何可能的局部密度或异质性。本文提出了一种基于局部决策圈（ldc）的增强广义PNN (EPNN)，克服了上述缺点，提高了其对数据噪声的鲁棒性。局部决策圈使EPNN能够将训练人口中存在的局部信息和非同质性结合起来。圆的半径限制了局部决策的贡献。在传统的PNN中，可以对扩散参数进行优化以获得最大的分类精度。在提出的EPNN中，对扩展参数和局部决策圆半径两个参数进行优化，使模型的性能最大化。使用虹膜数据、糖尿病数据和乳腺癌数据三个不同的基准分类问题，以及训练数据与测试数据的五种不同比例（90:10、80:20、70:30、60:40和50:50），比较EPNN与PNN的准确率和鲁棒性。EPNN为所有比率提供了一致的最准确结果。采用不同的信噪比对PNN和EPNN的鲁棒性进行了研究。在不同信噪比水平下，以及在训练数据与测试数据的所有比率下，EPNN的准确率始终高于PNN的准确率。
In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data.