From synapses to rules (original) (raw)
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Extracting rules from neural networks using symbolic algorithms: preliminary results
Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001, 2001
Although Artificial Neural Networks (ANNs) have satisfactory emploied in several problems, such as clustering, pattern recognition, dynamic systems control and prediction, they still suffer of a significant limitation. The representations they learn are not usually comprehensible to humans. Several techniques have been suggested to extract meaninful knowledge from trained ANNs. This paper proposes the use of symbolic learning to extract symbolic representations from trained ANNs. These algorithms are commonly used by the Machine Learning community. They learn concepts represented by propositional description. The procedure proposed is similar to that used by the Trepan algorithm (Craven, 1996). This algorithm extract comprehensible, symbolic representations (decision trees) from trained ANNs. Trepan queries a trained network to induce a decision tree that describes the concept represented by the ANN.
1998
This last decade multi-layer perceptrons (MLPs) have been widely used in classification tasks. Nevertheless, the difficulty of explaining their results represents a main drawback for their acceptance in critical domain applications such as medical diagnosis. In this context how can we trust a black box without any form of explanation capability ? To redress this situation, the internal representation of a multi-layer perceptron should be transformed into symbolic rules. Such a network is a neural expert system. In the field of symbolic rule extraction from neural networks Andrews et al. proposed a taxonomy to explain and compare the characteristics of the existing techniques. After having studied what we consider the main contribution of the domain we propose the new approach of extracting symbolic rules by precisely locating the discriminant frontiers between two classes. Basically, in our mathematical analysis we point out that a frontier is built according to an equation with one...
Extraction of Symbolic Rules From Artificial Neural Networks
Trans. Eng., Comput. Technol, 2005
Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, such as breast cancer, iris, diabetes, and season classification problems, demonstrate the effectiveness of the proposed approach with good generalization ability.
Extraction of logical rules from neural networks
Neural Processing Letters, 1998
Three neural-based methods for extraction of logical rules from data are presented. These methods facilitate conversion of graded response neural networks into networks performing logical functions. MLP2LN method tries to convert a standard MLP into a network performing logical operations (LN). C-MLP2LN is a constructive algorithm creating such MLP networks. Logical interpretation is assured by adding constraints to the cost function, forcing the weights to ±1 or 0. Skeletal networks emerge ensuring that a minimal number of logical rules are found. In both methods rules covering many training examples are generated before more specific rules covering exceptions. The third method, FSM2LN, is based on the probability density estimation. Several examples of performance of these methods are presented.
Rule extraction from Boolean artificial neural networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)
Rule extraction from classifiers treated as black boxes is an important topic in explainable artificial intelligence (XAI). It is concerned with finding rules that describe classifiers and that are understandable to humans, having the form of (I f ...T hen...Else). Neural network classifiers are one type of classifier where it is difficult to know how the inputs map to the decision. This paper presents a technique to extract rules from a neural network where the feature space is Boolean, without looking at the inner structure of the network. For such a network with a small feature space, a Boolean function describing it can be directly calculated, whilst for a network with a larger feature space, a sampling method is described to produce rule-based approximations to the behaviour of the network with varying granularity, leading to XAI. The technique is experimentally assessed on a dataset of cross-site scripting (XSS) attacks, and proves to give very high accuracy and precision, comparable to that given by the neural network being approximated.
Extraction of logical rules from training data using backpropagation networks
The 1st Online Workshop on …, 1996
Simple method for extraction of logical rules from neural networks trained with backpropagation algorithm is presented. Logical interpretation is assured by adding an additional term to the cost function, forcing the weight values to be ±1 or zero. Auxiliary constraint ensures that the training process strives to a network with maximal number of zero weights, which augmented by weight pruning yields a minimal number of logical rules extracted by means of weights analysis. Rules are generated consecutively, from most general, covering many training examples, to most specific, covering a few or even single cases. If there are any exceptions to these rules, they are being detected by additional neurons.
Symbolic Integration of Neural Classificators
The article describes a method in order to integrate the sub-symbolic classification, using neural networks, with symbolic rules. The aim of this work is to extract implicit knowledge embedded in neural networks building a hybrid system, the symbolic rules of which coming out clustering synaptic weights. This allows us to develop a diagnostic system that joins neural networks plasticity and symbolic information comprehensibility. The methodology used in this article is based on the ability of Towell and Shavlik Method to extract symbolic rules from a multilayer perceptron
Applying Genetic and Symbolic Learning Algorithms to Extract Rules from Artificial Neural Networks
Lecture Notes in Computer Science, 2004
Several research works have shown that Artificial Neural Networks -ANNs -have an appropriate inductive bias for several domains, since they can learn any input-output mapping, i.e., ANNs have the universal approximation property. Although symbolic learning algorithms have a less flexible inductive bias than ANNs, they are needed when a good understating of the decision process is essential, since symbolic ML algorithms express the knowledge induced using symbolic structures that can be interpreted and understood by humans. On the other hand, ANNs lack the capability of explaining their decisions, since the knowledge is encoded as real-valued weights and biases of the network. This encoding is difficult to be interpreted by humans. Aiming to take advantage of both approaches, this work proposes a method that extract symbolic knowledge, expressed as decision rules, from ANNs. The proposed method combines knowledge induced by several symbolic ML algorithms through the application of a Genetic Algorithm -GA. Our method is experimentally analyzed in a number of application domains. Results show that the method is able to extract symbolic knowledge having high fidelity with trained ANNs. The proposed method is also compared to TREPAN, another method for extracting knowledge from ANNs, showing promising results.