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Mining fuzzy rules using an Artificial Immune System with fuzzy partition learning

The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. Accuracy boosting relies on fuzzy partition learning. The performance, in terms of classification accuracy, of the proposed approach was compared with traditional classifier schemes: C4.5, Naive Bayes, K^*, Meta END, JRip, and Hyper Pipes. The result accuracy of these methods are significantly lower than accuracy of fuzzy rules obtained by method presented in this study (paired t-test, P<0.05).

Self-adaptation of parameters in a learning classifier system ensemble machine

Self-adaptation of parameters in a learning classifier system ensemble machine Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.

Playing a toy-grammar with GCS

Grammar-based Classifier System (GCS) is a new version of Learning Classifier Systems in which classifiers are represented by context-free grammar in Chomsky Normal Form (CNF). Discovering component of the GCS and fitness function were modified and applied for inferring a toy-grammar, a tiny natural language grammar expressed in CNF. The results obtained proved that proposed rules fertility improves performance of the GCS considerably.

Grammar-Based Classifier System for Recognition of Promoter Regions

Identifying bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters in Escherichia coli. Language of bacterial sequence can be described using formal system such a context-free grammar, and problem of promoter region recognition replaced by grammar induction. The accepted method for this problem is to use grammar-based classifier system (GCS).

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

BackgroundAmyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods.ResultsWe generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning).ConclusionsWe showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset proved representative enough to use simple statistical methods for testing the amylogenicity based only on six letter sequences. Statistical machine learning methods such as Alternating Decision Tree and Multilayer Perceptron can replace the energy based classifier, with advantage of very significantly reduced computational time and simplicity to perform the analysis. Additionally, a decision tree provides a set of very easily interpretable rules.

Short communication: Mining knowledge from data using Anticipatory Classifier System

The article demonstrates the capabilities of Anticipatory Classifier System in solving the data mining tasks. It is a first application of anticipatory learning to a real world exploration tasks. The results of experiments with Monks, Voting-record and WBC problems are shown as well as comparison of these results with other systems is presented. ACS handles these tasks quite well even though it is designed for a different kind of problems.

Learning context-free grammar using improved tabular representation

This paper describes an improved version of TBL algorithm [Y. Sakakibara, Learning context-free grammars using tabular representations, Pattern Recognition 38(2005) 1372-1383; Y. Sakakibara, M. Kondo, GA-based learning of context-free grammars using tabular representations, in: Proceedings of 16th International Conference in Machine Learning (ICML-99), Morgan-Kaufmann, Los Altos, CA, 1999] for inference of context-free grammars in Chomsky Normal Form. The TBL algorithm is a novel approach to overcome the hardness of learning context-free grammars from examples without structural information available. The algorithm represents the grammars by parsing tables and thanks to this tabular representation the problem of grammar learning is reduced to the problem of partitioning the set of nonterminals. Genetic algorithm is used to solve NP-hard partitioning problem. In the improved version modified fitness function and new delete specialized operator is applied. Computer simulations have been performed to determine improved a tabular representation efficiency. The set of experiments has been divided into 2 groups: in the first one learning the unknown context-free grammar proceeds without any extra information about grammatical structure, in the second one learning is supported by a partial knowledge of the structure. In each of the performed experiments the influence of partition block size in an initial population and the size of population at grammar induction has been tested. The new version of TBL algorithm has been experimentally proved to be not so much vulnerable to block size and population size, and is able to find the solutions faster than standard one.

Accuracy boosting induction of fuzzy rules with Artificial Immune Systems

The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using artificial immune system methods. Accuracy boosting relies on fuzzy partition learning. The modified algorithm was experimentally proved to be more accurate for all learning sets containing non-crisp attributes.

How to use crowding selection in grammar-based classifier system

The grammar-based classifier system (GCS) is a new version of learning classifier systems (LCS) in which classifiers are represented by context-free grammar in Chomsky normal form. GCS evolves one grammar during induction (the Michigan approach) which gives it the ability to find the proper set of rules very quickly. However it is quite sensitive to any variations of learning parameters. This paper investigates the role of crowding selection in GCS. To evaluate the performance of GCS depending on crowding factor and crowding subpopulation we used context-free language in the form of so-called toy language. The set of experiments was performed to obtain the answer for question in the title.

Collaborative filtering recommendation algorithm based on Hadoop and Spark

The aim of this work was to develop and compare recommendation systems which use the item-based collaborative filtering algorithm, based on Hadoop and Spark. Data for the research were gathered from a real social portal the users of which can express their preferences regarding the applications on offer. The Hadoop version was implemented with the use of the Mahout library which was an element of the Hadoop ecosystem. The authors original solution was implemented with the use of the Apache Spark platform and the Scala programming language. The applied similarity measure was the Tanimoto coefficient which provides the most precise results for the available data. The initial assumptions were confirmed as the solution based on the Apache Spark platform turned out to be more efficient.