Data Mining - Concepts and Techniques.

Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Frenay%20-%20Classification%20in%20the%20Presence%20of%20Label%20Noise%20-%202014.pdf

Classification in the Presence of Label Noise: A Survey

The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications.

Review Article: Recent Advances in Artificial Immune Systems: Models and Applications

Feature subset selection is of great importance in the field of data mining. The high dimension data makes testing and training of general classification methods difficult. In the present paper two filters approaches namely Gain ratio and Correlation based feature selection have been used to illustrate the significance of feature subset selection for classifying Pima Indian diabetic database (PIDD). The C4.5 tree uses gain ratio to determine the splits and to select the most important features. Genetic algorithm is used as search method with Correlation based feature selection as subset evaluating mechanism. The feature subset obtained is then tested using two supervised classification method namely, Back propagation neural network and Radial basis function network. Experimental results show that the feature subsets selected by CFS filter resulted in marginal improvement for both back propagation neural network and Radial basis function network classification accuracy when compared to feature subset selected by information gain filter.

Comparative study of attribute selection using gain ratio and correlation based feature selection

The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd.

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Choosing software metrics for defect prediction: an investigation on feature selection techniques

Machine learning techniques for automated musical genre classification is currently widely studied. With large collections of digital musical files, one approach to classification is to classify by musical genres such as pop, rock and classical in Western music. Beat, pitch and temporal related features are extracted from audio signals and various machine learning algorithms are applied for classification. Features that resulted in better classification accuracies for Traditional Malay Music (TMM), in comparison to western music, in a previous study were beat related features. However, only the J48 classifier was used and in this study we perform a more comprehensive investigation on improving the classification of TMM. In addition, feature selection was performed for dimensionality reduction. Classification accuracies using classifiers of varying paradigms on a dataset comprising ten TMM genres were obtained. Results identify potentially useful classifiers and show the impact of adding a feature selection phase for TMM genre classification.

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A study on feature selection and classification techniques for automatic genre classification of traditional Malay music

Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system.

Artificial Immune Recognition System (AIRS) has shown an effective performance on several machine learning problems In this study, the resource allocation method of AIRS was changed with a nonlinear method This new algorithm, AIRS with nonlinear resource allocation method, was used as a classifier in Traditional Malay Music (TMM) genre classification Music genre classification has a great important role in music information retrieval systems nowadays The proposed system consists of three stages: feature extraction, feature selection and finally using proposed algorithm as a classifier Based on results of conducted experiments, the obtained classification accuracy of proposed system is 886 % using 10 fold cross validation for TMM genre classification The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification

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Artificial Immune Recognition System with Nonlinear Resource Allocation Method and Application to Traditional Malay Music Genre Classification

Abstract Gravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of the initial population are clustered by K-means. Afterwards, new population is created and divided in different niches (or clusters) to expand the search space. The “loop in loop” technique guides the members of each niche to the optimum direction according to their clusters. This means that lighter members move faster towards the optimum direction of each cluster than the heavier members. For evaluations, KGSA is benchmarked on well-known functions and is compared with some of the state-of-the-art algorithms. Experiments show that KGSA provides better results than the other algorithms in finding local and global optima of constrained and unconstrained multimodal functions.

KGSA: A Gravitational Search Algorithm for Multimodal Optimization based on K-Means Niching Technique and a Novel Elitism Strategy

Artificial Immune Recognition System (AIRS) is immune inspired classifier that competes with famous classifiers. Many researches have been conducted to improve the accuracy of AIRS and to investigate the source of power of AIRS. Some of these researches have focused on resource allocation method of AIRS.This study investigates the difference between the accuracy of AIRS with fuzzy resource allocation and the accuracy of original AIRS, by using the reliable statistical method. The combination of ten fold cross validation and t-test was used as evaluation method and algorithms tested on ten benchmark datasets of UCI machine learning repository. Based on the results of experiments, using fuzzy resource allocation increases the accuracy of AIRS in majority of datasets but the increase is significant in minority of datasets.

http://www.jatit.org/volumes/research-papers/Vol5No1/1Vol5No4.pdf

Shahram Golzari

Papers

A study on feature selection and classification techniques for automatic genre classification of traditional Malay music

RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system.

Artificial Immune Recognition System with Nonlinear Resource Allocation Method and Application to Traditional Malay Music Genre Classification

KGSA: A Gravitational Search Algorithm for Multimodal Optimization based on K-Means Niching Technique and a Novel Elitism Strategy

Effect of fuzzy resource allocation method on airs classifier accuracy