Recursive neural network rule extraction for data with mixed attributes

In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.     

A GAs based approach for mining breast cancer pattern

Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.     

Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification

Polynomial neural networks have been known to exhibit useful properties as classifiers and universal approximators. In this study, we introduce a concept of polynomial-based radial basis function neural networks (P-RBF NNs), present a design methodology and show the use of the networks in classification problems. From the conceptual standpoint, the classifiers of this form can be expressed as a collection of “if-then” rules. The proposed architecture uses two essential development mechanisms. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of condition parts of the rules while the corresponding conclusions of the rules are formed by some polynomials. A detailed learning algorithm for the P-RBF NNs is developed. The proposed classifier is applied to two-class pattern classification problems. The performance of this classifier is contrasted with the results produced by the “standard” RBF neural networks. In addition, the experimental application covers a comparative analysis including several previous commonly encountered methods such as standard neural networks, SVM, SOM, PCA, LDA, C4.5, and decision trees. The experimental results reveal that the proposed approach comes with a simpler structure of the classifier and better prediction capabilities.     

Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems

Artificial neural networks often achieve high classification accuracy rates, but they are considered as black boxes due to their lack of explanation capability. This paper proposes the new rule extraction algorithm RxREN to overcome this drawback. In pedagogical approach the proposed algorithm extracts the rules from trained neural networks for datasets with mixed mode attributes. The algorithm relies on reverse engineering technique to prune the insignificant input neurons and to discover the technological principles of each significant input neuron of neural network in classification. The novelty of this algorithm lies in the simplicity of the extracted rules and conditions in rule are involving both discrete and continuous mode of attributes. Experimentation using six different real datasets namely iris, wbc, hepatitis, pid, ionosphere and creditg show that the proposed algorithm is quite efficient in extracting smallest set of rules with high classification accuracy than those generated by other neural network rule extraction methods.     

Fuzzy rules extraction from support vector machines for multi-class classification

This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems, named FREx_SVM. SVMs have been used in a variety of applications. However, they are considered “black box models,” where no interpretation about the input–output mapping is provided. Some methods to reduce this limitation have already been proposed, but they are restricted to binary classification problems and to the extraction of symbolic rules with intervals or functions in their antecedents. In order to improve the interpretability of the generated rules, this paper presents a new model for extracting fuzzy rules from a trained SVM. The proposed model is suited for classification in multi-class problems and includes a wrapper feature selection algorithm. It is evaluated in four benchmark databases, and results obtained demonstrate its capacity to generate a reduced set of interpretable fuzzy rules that explains both the classification database and the influence of each input variable on the determination of the final class.     

Rule revision with recurrent neural networks

Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge (or rules) into recurrent neural networks, we show that recurrent neural networks are able to perform rule revision. Rule revision is performed by comparing the inserted rules with the rules in the finite-state automata extracted from trained networks. The results from training a recurrent neural network to recognize a known non-trivial, randomly-generated regular grammar show that not only do the networks preserve correct rules but that they are able to correct through training inserted rules which were initially incorrect (i.e. the rules were not the ones in the randomly generated grammar)     

Knowledge distilling based model compression and feature learning in fault diagnosis

Recently, there has been interest in developing diagnosis methods that combine model-based and data-driven diagnosis. In both approaches, selecting the relevant measurements or extracting important features from historical data is a key determiner of the success of the algorithm. Recently, deep learning methods have been effective in automating the feature selection process. Autoencoders have been shown to be an effective neural network configuration for extracting features from complex data, however, they may also learn irrelevant features. In addition, end-to-end classification neural networks have also been used for diagnosis, but like autoencoders, this method may also learn unimportant features thus making the diagnostic inference scheme inefficient. To rapidly extract significant fault features, this paper employs end-to-end networks and develops a new feature extraction method based on importance analysis and knowledge distilling. First, a set of cumbersome neural network models are trained to predict faults and some of their internal values are defined as features. Then an occlusion-based importance analysis method is developed to select the most relevant input variables and learned features. Finally, a simple student neural network model is designed based on the previous analysis results and an improved knowledge distilling method is proposed to train the student model. Because of the way the cumbersome networks are trained, only fault features are learned, with the importance analysis further pruning the relevant feature set. These features can be rapidly generated by the student model. We discuss the algorithms, and then apply our method to two typical dynamic systems, a communication system and a 10-tank system employed to demonstrate the proposed approach.     

Neural-Based Learning Classifier Systems

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

Rough sets for adapting wavelet neural networks as a new classifier system

Classification is an important theme in data mining. Rough sets and neural networks are two techniques applied to data mining problems. Wavelet neural networks have recently attracted great interest because of their advantages over conventional neural networks as they are universal approximations and achieve faster convergence. This paper presents a hybrid system to extract efficiently classification rules from decision table. The neurons of such hybrid network instantiate approximate reasoning knowledge gleaned from input data. The new model uses rough set theory to help in decreasing the computational effort needed for building the network structure by using what is called reduct algorithm and a rules set (knowledge) is generated from the decision table. By applying the wavelets, frequencies analysis, rough sets and dynamic scaling in connection with neural network, novel and reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing with traditional rough set and neural networks approaches.     

An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems.

There are numerous combinations of neural networks (NNs) and evolutionary algorithms (EAs) used in classification problems. EAs have been used to train the networks, design their architecture, and select feature subsets. However, most of these combinations have been tested on only a few data sets and many comparisons are done inappropriately measuring the performance on training data or without using proper statistical tests to support the conclusions. This paper presents an empirical evaluation of eight combinations of EAs and NNs on 15 public-domain and artificial data sets. Our objective is to identify the methods that consistently produce accurate classifiers that generalize well. In most cases, the combinations of EAs and NNs perform equally well on the data sets we tried and were not more accurate than hand-designed neural networks trained with simple backpropagation.     

Learning Premises of Fuzzy Rules for Knowledge Acquisition in Classification Problems

A key issue in building fuzzy classification systems is the specification of rule conditions, which determine the structure of a knowledge base. This paper presents a new approach to automatically extract classification knowledge from numerical data by means of premise learning. A genetic algorithm is employed to search for premise structure in combination with parameters of membership functions of input fuzzy sets to yield optimal conditions of classification rules. The major advantage of our work is that a parsimonious knowledge base with a low number of rules can be achieved. The practical applicability of the proposed method is examined by computer simulations on two well-known benchmark problems of Iris Data and Cancer Data classification. Received 11 February 1999 / Revised 13 January 2001 / Accepted in revised form 13 February 2001     

A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner

Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in this paper for generating classification rules from feed forward type ANNs. Although there are several approaches in the literature for classification rule extraction from ANNs, the present approach is fundamentally different from them. In the previous studies, ANN training and rule extraction is generally performed independently in a sequential (hierarchical) manner. However, in the present study, training and rule extraction phases are integrated within a multiple objective evaluation framework for generating accurate classification rules directly. The proposed approach makes use of differential evolution algorithm for training and touring ant colony optimization algorithm for rule extracting. The proposed algorithm is named as DIFACONN-miner. Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.     

Extracting rules from trained neural networks

Presents an algorithm for extracting rules from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural network whose output function is monotone such as a sigmoid function. Therefore, the algorithm can be applied to multilayer neural networks, recurrent neural networks and so on. It does not depend on training algorithms, and its computational complexity is polynomial. The basic idea is that the units of neural networks are approximated by Boolean functions. But the computational complexity of the approximation is exponential, and so a polynomial algorithm is presented. The author has applied the algorithm to several problems to extract understandable and accurate rules. The paper shows the results for the votes data, mushroom data, and others. The algorithm is extended to the continuous domain, where extracted rules are continuous Boolean functions. Roughly speaking, the representation by continuous Boolean functions means the representation using conjunction, disjunction, direct proportion, and reverse proportion. This paper shows the results for iris data.     

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Purpose

Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.

Method

Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function.

Results

The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers.     

用神经网络驱动的模糊推理入侵检测方法

提出了神经网络驱动模糊推理的入侵检测方法,利用神经网络的学习能力,对不清楚规则的复杂系统的输入输出特性进行适当的非线性划分,自动形成舰则集和相应的隶属关系,克服了在多维空间上经验性的确定隶属函数的困难。对于神经网络的训练数据,采用人工数据,克服了神经网络监督学习和获取已知输出的训练数据的困难。试验证明,这种技术具有很好的灵敏度和鲁棒性,而且,能够检测出未知的入侵行为。     

一种基于神经网络的知识获取方法研究与应用

提出了一种基于神经网络的知识获取方法,该方法利用语言神经元,对具有开区域的连续输入变量,自动产生相应的语言变量输出,讨论了相应的神经网络训练和知识获取方法,所获取的知识以If-Then的规则形式表示,具有简洁、紧凑、不必进一步化简、易于理解等特点,并给出一个在教学型专家系统中获取专家领域知识的应用实例。     

Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks

Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous system. They have the ability of predicting, learning from experiences and generalizing from previous examples. An important drawback of ANNs is their very limited explanation capability, mainly due to the fact that knowledge embedded within ANNs is distributed over the activations and the connection weights. Therefore, one of the main challenges in the recent decades is to extract classification rules from ANNs. This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. A soft computing based algorithm is developed to generate fuzzy rules based on a data mining tool (DIFACONN-miner), which was recently developed by the authors. Fuzzy DIFACONN-miner algorithm can extract fuzzy classification rules from datasets containing both categorical and continuous attributes. Experimental research on the benchmark datasets and comparisons with other fuzzy rule based classification (FRBC) algorithms has shown that the proposed algorithm yields high classification accuracies and comprehensible rule sets.     

Extracting decision trees from trained neural networks

In this paper we present a methodology for extracting decision trees from input data generated from trained neural networks instead of doing it directly from the data. A genetic algorithm is used to query the trained network and extract prototypes. A prototype selection mechanism is then used to select a subset of the prototypes. Finally, a standard induction method like ID3 or C5.0 is used to extract the decision tree. The extracted decision trees can be used to understand the working of the neural network besides performing classification. This method is able to extract different decision trees of high accuracy and comprehensibility from the trained neural network.