Rule Extraction from Opaque Models-- A Slightly Different Perspective

Rule extraction based on PROMETHEE-assisted multi-objective genetic algorithm for generating interpretable neural networks

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface (ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm (ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.

Data Mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. One of the most commonly used techniques in data mining is Artificial Neural Network due to its high performance in many application domains. Despite many advantages of Artificial Neural Network, one of its main drawbacks is its inherent black box nature which is the main problem of using Artificial Neural Network in data mining. Therefore, this paper proposes a rule extraction algorithm from neural network using classified and misclassified data to convert the black box nature of Artificial Neural Network into a white box. The proposed algorithm is a modification of the existing algorithm, Rule Extraction by Reverse Engineering (RxREN). The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses both classified as well as misclassified data to find out the data ranges of significant attributes in respective classes, which is the innovation of the proposed algorithm. The experimental results clearly show that the performance of the proposed algorithm is superior to existing algorithms.

The main challenge in using supervised neural networks in data mining applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquisition from supervised neural networks employed for classification problems is presented. An algorithm for rule extraction from neural networks, based on the RX algorithm is developed. This algorithm, named modified RX, is experimentally evaluated in two different domains: Iris Plants Database and Pima Indians Diabetes Database. The results are compared to those obtained by classification trees. As far as the efficacy is concerned, one observes that the successful application of the algorithm mainly depends on the knowledge representation acquired by the connectionist model, whereas the efficiency only depends on the neural network training time.

Neural networks have been applied in various domain including science, commerce, medicine, and industry. However, The knowledge learned by a trained neural network is difficult to understand. This paper proposes a Boolean algebra based algorithm to extract comprehensible Boolean rules from supervised feed-forward neural networks to uncover the black-boxed knowledge. This algorithm is called the BAB-BB rule extraction algorithm, which stands for a Boolean algebra based rule extraction algorithm for neural networks with binary and bipolar inputs. Decomposition techniques and interval arithmetic are used in the algorithm. First, each neuron associated with its inputs is analyzed and a Boolean function, describing the activation rule from its inputs to the neuron, is derived. These Boolean functions are merged into an aggregated Boolean rule according to the network topology. The Boolean rule is then further simplified by Boolean algebra operations. During the rule extraction procedure, redundant hidden neurons can be detected and removed without affecting the original function of the neural network. Examples of unipolar and bipolar inputs are presented to demonstrate the use of our algorithm. Finally, the Exclusive OR problem is presented and solved by our algorithm. Results show that our BAB-BB algorithm is practicable and of high efficiency.

How to extract rules from trained SVMs has become an important preprocessing technique for data mining, pattern classification, and so on. There are two key problems required to be solved in the SVM based classification rule extraction, i.e. the attribute selection and the discretization to continuous attributes. In this paper, the differential characteristic of SVR (Support vector regression) is discussed. A new measure for determining the importance level of the attributes based on the trained SVR classifiers is proposed. Based on this new measure, a new approach for rule extraction from trained SVR classifiers is proposed. A new algorithm for rule extraction is given. The performance of the new approach is demonstrated by several computing cases. Experiment results show that the proposed approach can improve the validity of the extracted rules remarkably compared to other rule extracting approaches, especially for complicated classification problems.

Artificial Neural Network (ANN) is one of the powerful techniques of machine learning. It has shown its effectiveness in both prediction and classification problems. However, in some fields there is still some reticence towards their use mainly the fact that they do not justify their answers. The lack of transparency on how ANN makes decisions motivated us to develop our rule extraction algorithm that extracts comprehensible rules with high accuracy and high fidelity. The aim is to generate a set of rules that mimic the decision of ANN and cover a larger set of patterns. The obtained rule sets should satisfy a well-balanced trade-off between the fidelity, the accuracy and the comprehensibility. The proposed algorithm consists of a three steps: ANN learning phase, rule extraction phase and rule simplification phase. The rule extraction phase is based on the extraction of the association rules while the rules simplification procedure is based on the laws of Boolean algebra. To evaluate the performance of our algorithm, the system has been studied using four datasets, and then compared with other rule extraction methods. The results show that our proposal offers a small set of rules having the highest accuracy and fidelity values.

Representing the knowledge learned by neural networks in the form of interpretable rules is a prudent technique to justify the decisions made by neural networks. Heretofore many algorithms exist to extract symbolic rules from neural networks, but among them, a few extract rules from deep neural networks trained using deep learning techniques. So, this paper proposes an algorithm to extract rules from a multi-hidden layer neural network, pre-trained using deep belief network and fine-tuned using back propagation. The algorithm analyzes each node of a layer and extracts knowledge from each layer separately. The process of knowledge extraction from the first hidden layer is different from the other layers. Consecutively, the algorithm combines all the knowledge extracted and refines them to construct a final ruleset consisting of symbolic rules. The algorithm further subdivides the subspace of a rule in the ruleset if it satisfies certain conditions. Results show that the algorithm extracted rules with higher accuracy compared to some existing rule extraction algorithms. Other than accuracy, the efficacy of the extracted rules is also validated with fidelity and various other performance measures.

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

A modified fuzzy min–max neural network with rule extraction and its application to fault detection and classification

Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate data. Many of these use non-linear models to handle complex data-relationships in the input data. Their lack of transparency, however, is problematic since comprehensibility is a key requirement in many potential application domains. Rule-extraction algorithms have been proposed to solve this problem for classification by extracting comprehensible rule sets from the often better performing, complex models. We present a new pedagogical rule extraction algorithm for regression, based on active learning, which can be combined with any existing rule induction technique. Empirical results show that the proposed ALPA-R rule extraction method improves on classical rule induction techniques, both in accuracy and fidelity.

Parallel extraction of association rules from genomics data

In this chapter, FMM is endowed with a rule extraction algorithm. With the rule extraction algorithm, FMM is able to explain its predictions using fuzzy if-then rules, thus overcoming the “black-box” phenomenon as suffered by most NN models. Applicability of FMM to fault diagnosis tasks in a power generation plant has been examined. The potential of FMM in learning and predicting faults in complex processes as well as in providing a comprehensible explanation for its predictions has been demonstrated in two experiments. The proposed rule extraction algorithm is able to yield a comprehensible rule set. The extracted rules have been verified as meaningful and are in line with the domain knowledge as well as experts’ opinions. Further research work will concentrate on the aspects of implementation, validation, and verification of FMM as a useful, robust, and intelligent fault diagnosis tool in a variety of application domains.

This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable accuracy, based on decision tree performance on bootstrap samples of the training set. Next, a normal GP evolution is started with the aim of producing an accurate GP. At even intervals, the best GP in the population is evaluated against the accuracy benchmark. If the GP has higher accuracy than the benchmark, the evolution continues normally until the maximum number of generations is reached. If the accuracy is lower than the benchmark, two things happen. First, the fitness function is modified to allow larger GPs, able to represent more complex models. Secondly, a decision tree with increased size and trained on a bootstrap of the training data is injected into the population. The experiments show that the hybrid solution of injecting decision trees into a GP population gives synergetic effects producing results that are better than using either technique separately. The results, from 18 UCI data sets, show that the proposed method clearly outperforms normal GP, and is significantly better than the standard decision tree algorithm.