José Luis Subirats

A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions

A new algorithm for obtaining efficient architectures composed of threshold gates that implement arbitrary Boolean functions is introduced. The method reduces the complexity of a given target function by splitting the function according to the variable with the highest influence. The procedure is iteratively applied until a set of threshold functions is obtained, leading to reduced depth architectures, in which the obtained threshold functions form the nodes and a and or or function is the output of the architecture. The algorithm is tested on a large set of benchmark functions and the results compared to previous existing solutions, showing a considerable reduction on the number of gates and levels of the obtained architectures. An extension of the method for partially defined functions is also introduced and the generalization ability of the method is analyzed.

C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons

C-Mantec is a novel neural network constructive algorithm that combines competition between neurons with a stable modified perceptron learning rule. The neuron learning is governed by the thermal perceptron rule that ensures stability of the acquired knowledge while the architecture grows and while the neurons compete for new incoming information. Competition makes it possible that even after new units have been added to the network, existing neurons still can learn if the incoming information is similar to their stored knowledge, and this constitutes a major difference with existing constructing algorithms. The new algorithm is tested on two different sets of benchmark problems: a Boolean function set used in logic circuit design and a well studied set of real world problems. Both sets were used to analyze the size of the constructed architectures and the generalization ability obtained and to compare the results with those from other standard and well known classification algorithms. The problem of overfitting is also analyzed, and a new built-in method to avoid its effects is devised and successfully applied within an active learning paradigm that filter noisy examples. The results show that the new algorithm generates very compact neural architectures with state-of-the-art generalization capabilities.

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

BackgroundExtracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information.MethodsDue to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome.ResultsBetter cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings.ConclusionsThis study shows that if prediction accuracy is the objective, the GA-based approach lead to better results respect to the SFS approach, independently of the classifier used. Regarding classifiers, even if C-MANTEC did not achieve the best overall results, the performance was competitive with a very robust behaviour in terms of the parameters of the algorithm, and thus it can be considered as a candidate technique for future studies.

Smart sensor/actuator node reprogramming in changing environments using a neural network model

The techniques currently developed for updating software in sensor nodes located in changing environments require usually the use of reprogramming procedures, which clearly increments the costs in terms of time and energy consumption. This work presents an alternative to the traditional reprogramming approach based on an on-chip learning scheme in order to adapt the node behaviour to the environment conditions. The proposed learning scheme is based on C-Mantec, a novel constructive neural network algorithm especially suitable for microcontroller implementations as it generates very compact size architectures. The Arduino UNO board was selected to implement this learning algorithm as it is a popular, economic and efficient open source single-board microcontroller. C-Mantec has been successfully implemented in a microcontroller board by adapting it in order to overcome the limitations imposed by the limited resources of memory and computing speed of the hardware device. Also, this work brings an in-depth analysis of the solutions adopted to overcome hardware resource limitations in the learning algorithm implementation (e.g., data type), together with an efficiency assessment of this approach when the algorithm is tested on a set of circuit design benchmark functions. Finally, the utility, efficiency and versatility of the system is tested in three different-nature case studies in which the environmental conditions change its behaviour over time.

Multiclass Pattern Recognition Extension for the New C-Mantec Constructive Neural Network Algorithm

The new C-Mantec algorithm constructs compact neural network architectures for classsification problems, incorporating new features like competition between neurons and a built-in filtering stage of noisy examples. It was originally designed for tackling two class problems and in this work the extension of the algorithm to multiclass problems is analyzed. Three different approaches are investigated for the extension of the algorithm to multi-category pattern classification tasks: One-Against-All (OAA), One-Against-One (OAO), and P-against-Q (PAQ). A set of different sizes benchmark problems is used in order to analyze the prediction accuracy of the three multi-class implemented schemes and to compare the results to those obtained using other three standard classification algorithms.

Implementation of the C-mantec neural network constructive algorithm in an arduino uno microcontroller

A recently proposed constructive neural network algorithm, named C-Mantec, is fully implemented in a Arduino board. The C-Mantec algorithm generates very compact size neural architectures with good prediction abilities, and thus the board can be potentially used to learn on-site sensed data without needing to transmit information to a central control unit. An analysis of the more difficult steps of the implementation is detailed, and a test is carried out on a set of benchmark functions normally used in circuit design to show the correct functioning of the implementation.

Constructive neural networks to predict breast cancer outcome by using gene expression profiles

Gene expression profiling strategies have attracted considerable interest from biologist due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Methods using artifical neural networks (ANNs) were developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. The problematic of using a stepwise forward selection ANN method is that it needs many different parameters depending on the complexity of the problem and choosing the proper neural network architecture for a given classification problem is not a trivial problem. A novel constructive neural networks algorithm (CMantec) is applied in order to predict estrogen receptor status by using data from microarrays experiments. The obtained results show that CMantec model clearly outperforms the ANN model both in process execution time as in the final prognosis accuracy. Therefore, CMantec appears as a powerful tool to identify gene signatures that predict the ER status for a given patient.

Early breast cancer prognosis prediction and rule extraction using a new constructive neural network algorithm

Breast cancer relapse prediction is an important step in the complex decision-making process of deciding the type of treatment to be applied to patients after surgery. Some non-linear models, like neural networks, have been successfully applied to this task but they suffer from the problem of extracting the underlying rules, and knowing how the methods operate can help to a better understanding of the cancer relapse problem. A recently introduced constructive algorithm (DASG) that creates compact neural network architectures is applied to a dataset of early breast cancer patients with the aim of testing the predictive ability of the new method. The DASG method works with Boolean input data and for that reason a transformation procedure was applied to the original data. The degradation in the predictive performance due to the transformation of the data is also analyzed using the new method and other standard algorithms.

Optimal synthesis of boolean functions by threshold functions

We introduce a new method for obtaining optimal architectures that implement arbitrary Boolean functions using threshold functions. The standard threshold circuits using threshold gates and weights are replaced by nodes computing directly a threshold function of the inputs. The method developed can be considered exhaustive as if a solution exist the algorithm eventually will find it. At all stages different optimization strategies are introduced in order to make the algorithm as efficient as possible. The method is applied to the synthesis of circuits that implement a flip-flop circuit and a multi-configurable gate. The advantages and disadvantages of the method are analyzed.

Active Learning Using a Constructive Neural Network Algorithm

Constructive neural network algorithms suffer severely from overfitting noisy datasets as, in general, they learn the set of examples until zero error is achieved. We introduce in this work a method for detect and filter noisy examples using a recently proposed constructive neural network algorithm. The method works by exploiting the fact that noisy examples are harder to be learnt, needing a larger number of synaptic weight modifications than normal examples. Different tests are carried out, both with controlled experiments and real benchmark datasets, showing the effectiveness of the approach.