# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 2 Related Work  
Many single-label classification algorithms have been proposed. The extended nearest neighbor (ENN) method [39], unlike the classic KNN, makes the prediction in a \"two-way communication\" style. It considers not only what are the nearest neighbors of the input pattern, but also those of which the input pattern is their nearest neighbors. The Natural Neighborhood Based Classification Algorithm (NNBCA) [40] provides a good classification result without artificially selecting the neighborhood parameter. Unlike KNN, it predicts different k for different samples. An enhanced general fuzzy min-max neural network (EGFM) classification model is proposed in [19] to perform supervised classification of data. New hyperbox expansion, overlap, and contraction rules are used to overcome some unidentified cases in some regions. SaE-ELM [41] is an improved algorithm of RVFL networks. In SaE-ELM, the hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore-Penrose generalized inverse. In [42], basis functions in an RBF network are interpreted as probability density functions. The weights are seen as prior probabilities. Models that output class conditional densities or mixture densities were proposed. In [24], an RBF network based on KNN with adaptive selection radius is proposed. An adaptive selection radius is calculated according to the population density sampling method. The RBF network is trained to locate the sound source by solving nonlinear equations about the time difference between arrival and source of the sound. Several problem transformation methods exist for multi-label classification. One popular transformation method, called binary relevance [43], transforms the original training dataset into \(p\) datasets, where \(p\) is the number of categories involved with the dataset. Each resulting dataset contains all the patterns of the original dataset, with each pattern assigned to one of the two labels: \"belonging to\" or \"not belonging to\" a particular category. Since the resulting datasets are all single-labeled, any single-label classification technique is applicable to them. The label powerset (LP) transformation [44] method creates one binary classifier for every label combination present in the training set. For example, eight possible label combinations are created if the number of categories associated with the original dataset is three. Ensemble methods were developed to create multi-label ensemble classifiers [45; 46]. A set of multi-class single-label classifiers are created. For an input example, each classifier outputs a single class. These predictions are then combined by an ensemble method. Some classification algorithms/models have been adapted to the multi-label task, without requiring problem transformations. Clare [29] is an adapted C4.5 version for multi-label classification. Other decision tree classification methods based on multi-valued attributes were also developed [47]. [PERSON] et al. [32] propose several multi-label learning algorithms including back-propagation multi-label learning (BP-MLL) [48], multi-label k-nearest neighbors (ML-KNN) [49] and multi-label RBF (ML-RBF) [50]. BP-MLL is a multi-label version of the back-propagation neural network. To take care of multi-labels, label co-occurrence is incorporated into the pairwise ranking loss function. However, it has a complex global error function to be minimized. ML-KNN is a lazy learning algorithm which requires a big run-time search. ML-RBF is a multi-label RBF neural network which is an extension of the traditional RBF learning algorithm. Multi-label with Fuzzy Relevance Clustering (ML-FRC), proposed by [PERSON] and [PERSON] [51], is a fuzzy relevance clustering based method for the task of multi-labeltext categorization. [PERSON] et al.