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Journal articles on the topic 'Classifier systems'

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Published: 4 June 2021
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1

ISHIBUCHI, Hisao. "Fuzzy Classifier Systems." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 4 (1998): 613–25. http://dx.doi.org/10.3156/jfuzzy.10.4_33.

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2

Geyer-Schulz, Andreas. "Holland classifier systems." ACM SIGAPL APL Quote Quad 25, no. 4 (June 8, 1995): 43–55. http://dx.doi.org/10.1145/206944.206955.

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3

Bull, Larry, Pler Luca Lanzi, and Wolfgang Stolzmann. "Learning Classifier Systems." Soft Computing - A Fusion of Foundations, Methodologies and Applications 6, no. 3-4 (June 1, 2002): 143. http://dx.doi.org/10.1007/s005000100110.

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4

Anagnostopoulos, Theodoros, and Christos Skourlas. "Ensemble majority voting classifier for speech emotion recognition and prediction." Journal of Systems and Information Technology 16, no. 3 (August 5, 2014): 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.

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Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one-against-all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.
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5

Amerineni, Rajesh, Resh S. Gupta, and Lalit Gupta. "Multimodal Object Classification Models Inspired by Multisensory Integration in the Brain." Brain Sciences 9, no. 1 (January 2, 2019): 3. http://dx.doi.org/10.3390/brainsci9010003.

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Two multimodal classification models aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli are introduced. The feature-integrating model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The decision-integrating model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions.
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6

Kovacs, T. "Learning classifier systems resources." Soft Computing - A Fusion of Foundations, Methodologies and Applications 6, no. 3-4 (June 1, 2002): 240–43. http://dx.doi.org/10.1007/s005000100119.

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7

Tomlinson, Andy, and Larry Bull. "Symbiogenesis in Learning Classifier Systems." Artificial Life 7, no. 1 (January 2001): 33–61. http://dx.doi.org/10.1162/106454601300328016.

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Symbiosis is the phenomenon in which organisms of different species live together in close association, resulting in a raised level of fitness for one or more of the organisms. Symbiogenesis is the name given to the process by which symbiotic partners combine and unify, that is, become genetically linked, giving rise to new morphologies and physiologies evolutionarily more advanced than their constituents. The importance of this process in the evolution of complexity is now well established. Learning classifier systems are a machine learning technique that uses both evolutionary computing techniques and reinforcement learning to develop a population of cooperative rules to solve a given task. In this article we examine the use of symbiogenesis within the classifier system rule base to improve their performance. Results show that incorporating simple rule linkage does not give any benefits. The concept of (temporal) encapsulation is then added to the symbiotic rules and shown to improve performance in ambiguous/non-Markov environments.
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8

Croft, William. "Semantic universals in classifier systems." WORD 45, no. 2 (August 1994): 145–71. http://dx.doi.org/10.1080/00437956.1994.11435922.

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9

Mitlöhner, Johann. "Classifier systems and economic modeling." ACM SIGAPL APL Quote Quad 26, no. 4 (June 15, 1996): 77–86. http://dx.doi.org/10.1145/253417.253396.

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10

Dam, H. H., H. A. Abbass, C. Lokan, and Xin Yao. "Neural-Based Learning Classifier Systems." IEEE Transactions on Knowledge and Data Engineering 20, no. 1 (January 2008): 26–39. http://dx.doi.org/10.1109/tkde.2007.190671.

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11

Kuncheva, Ludmila I. "Diversity in multiple classifier systems." Information Fusion 6, no. 1 (March 2005): 3–4. http://dx.doi.org/10.1016/j.inffus.2004.04.009.

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12

Booker, L. B., D. E. Goldberg, and J. H. Holland. "Classifier systems and genetic algorithms." Artificial Intelligence 40, no. 1-3 (September 1989): 235–82. http://dx.doi.org/10.1016/0004-3702(89)90050-7.

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13

Sigaud, Olivier, and Stewart W. Wilson. "Learning classifier systems: a survey." Soft Computing 11, no. 11 (March 29, 2007): 1065–78. http://dx.doi.org/10.1007/s00500-007-0164-0.

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14

Forrest, Stephanie, and John H. Miller. "Emergent behavior in classifier systems." Physica D: Nonlinear Phenomena 42, no. 1-3 (June 1990): 213–27. http://dx.doi.org/10.1016/0167-2789(90)90075-z.

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15

Biggio, Battista, Giorgio Fumera, and Fabio Roli. "Multiple classifier systems for robust classifier design in adversarial environments." International Journal of Machine Learning and Cybernetics 1, no. 1-4 (October 12, 2010): 27–41. http://dx.doi.org/10.1007/s13042-010-0007-7.

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16

Saeh, Ibrahim, Wazir Mustafa, and Nasir Al-geelani. "New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 870. http://dx.doi.org/10.11591/ijece.v6i2.9572.

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This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.
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Saeh, Ibrahim, Wazir Mustafa, and Nasir Al-geelani. "New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 870. http://dx.doi.org/10.11591/ijece.v6i2.pp870-876.

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This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.
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18

Husin, Abdullah, and Ku Ruhana Ku-Mahamud. "Ant System and Weighted Voting Method for Multiple Classifier Systems." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4705. http://dx.doi.org/10.11591/ijece.v8i6.pp4705-4712.

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Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the strength of the classifiers prior to voting. Experiments were carried out using k-NN ensembles on benchmark datasets from the University of California, Irvine, to evaluate the credibility of the proposed method. Experimental results showed that the proposed method has successfully constructed better k-NN ensembles. Further more the proposed method can be used to develop other multiple classifier systems.
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19

Liu, Su Houn, Hsiu Li Liao, Shih Ming Pi, and Chih Chiang Kao. "Patent Classification Using Hybrid Classifier Systems." Advanced Materials Research 187 (February 2011): 458–63. http://dx.doi.org/10.4028/www.scientific.net/amr.187.458.

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Patents are distributed through hundreds of collections, divided up by general area. A hybrid classifier system thus can be a powerful solution to difficult patent classification problems. In this study, we present a system for classifying patent documents on a hybrid approach by combining multiple text classifiers (Naïve Bayes, KNN and Rocchio). Decisions made by various text classifiers can be combined by voting and sampling mechanisms in the system. A prototype system was developed and tested in a real world task. The results have indicated that the accuracy of the hybrid approach is more stable than that of any of the three individual text classifiers.
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20

Wilson, Stewart W. "Classifier Fitness Based on Accuracy." Evolutionary Computation 3, no. 2 (June 1995): 149–75. http://dx.doi.org/10.1162/evco.1995.3.2.149.

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In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X × A → P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general, subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is desirable.
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21

SCIARRONE, FILIPPO. "AN EXTENSION OF THE Q DIVERSITY METRIC FOR INFORMATION PROCESSING IN MULTIPLE CLASSIFIER SYSTEMS: A FIELD EVALUATION." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 06 (November 2013): 1350049. http://dx.doi.org/10.1142/s0219691313500495.

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Nowadays, in pattern recognition and classification, does not exist a dominant classifier for all data distributions. Also, data distribution of the task at hand is usually unknown, i.e. there is no algorithm achieving the best accuracy for all situations. An answer and a challenge to this problem is to build ensembles of classifiers working together, i.e. Multiple Classifier Systems, instead of building and running different classifiers separately: Multiple Classifier Systems can show better performance than a single classifier, provided a careful choice of the individual classifiers composing it. Furthermore, diversity among single classifiers, measured by some diversity metrics, is known to be a necessary condition to improve the ensemble performance. In this paper we extend the use of one of the most used diversity metrics, that is the Q diversity metric, from an oracle output to a soft output for the choice of the best classifier ensemble. We introduce the Qt diversity metric, i.e. an extension of the Q metric to multi-label and multi-ranking Multiple Classifier Systems. A field evaluation of this metric is presented in a text categorization case study, using as a test set the standard document corpus Reuters 21578 ModApte 10. Our results strengthen the use of the extended metric in multi-label and multi-ranking Multiple Classifier Systems.
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22

Volkodav, Vladimir A., and Ivan A. Volkodav. "Development of the structure and composition of a building information classifier towards the application of BIM technologies." Vestnik MGSU, no. 6 (June 2020): 867–906. http://dx.doi.org/10.22227/1997-0935.2020.6.867-906.

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Abstract Introduction. Various building information classification systems are used internationally; their critical analysis makes it possible to highlight basic requirements applicable to the Russian classifier and substantiate its structure and composition. Materials and methods. Modern international building information classification systems, such as OmniClass (USA), Uniclass 2015 (UK), CCS (Denmark), and CoClass (Sweden), are considered in the article. Their structure, composition, methodological fundamentals are analyzed. In addition to international classification systems, Russian construction information classifiers are analyzed. Results. The structure of a building information classifier has been developed and tailored to the needs of BIM (building information modeling) and national regulatory and technical requirements. The classifier’s structure complies with the one recommended by ISO 12006-2:2015. Its composition has regard to the requirements that apply to the aggregation and unification of Russian classifiers, and it also benefits from the classifiers developed for and used by the construction industry. The proposed building information classifier has four basic categories and 21 basic classes. Conclusions. The proposed structure and composition of a building information classifier represent a unified and universal tool for communicating building information or presenting it in the standardized format in the consolidated information space designated for information models needed to manage life cycles of major construction projects.
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23

Bollacker, Kurt Dewitt, and Joydeep Ghosh. "Knowledge reuse in multiple classifier systems." Pattern Recognition Letters 18, no. 11-13 (November 1997): 1385–90. http://dx.doi.org/10.1016/s0167-8655(97)00087-1.

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24

Smith, Robert E. "Memory Exploitation in Learning Classifier Systems." Evolutionary Computation 2, no. 3 (September 1994): 199–220. http://dx.doi.org/10.1162/evco.1994.2.3.199.

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Learning classifier systms (LCSs) offer a unique opportunity to study the adaptive exploitation of memory. Because memory is manipulated in the form of simple internal messages in the LCS, one can easily and carefully examine the development of a system of internal memory symbols. This study examines the LCS applied to a problem whose only performance goal is the effective exploitation of memory. Experimental results show that the genetic algorithm forms a relatively effective set of internal memory symbols, but that this effectiveness is directly limited by the emergence of parasite rules. The results indicate that the emergence of parasites may be an inevitable consequence in a system that must evolve its own set of internal memory symbols. The paper's primary conclusion is that the emergence of parasites is a fundamental obstacle in such problems. To overcome this obstacle, it is suggested that the LCS must form larger, multirule structures. In such structures, parasites can be more accurately evaluated and thus eliminated. This effect is demonstrated through a preliminary evaluation of a classifier corporation scheme. Final comments present future directions for research on memory exploitation in the LCS and similar evolutionary computing systems.
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25

Dara, Rozita A., Mohamed S. Kamel, and Nayer Wanas. "Data dependency in multiple classifier systems." Pattern Recognition 42, no. 7 (July 2009): 1260–73. http://dx.doi.org/10.1016/j.patcog.2008.11.035.

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26

Tin Kam Ho, J. J. Hull, and S. N. Srihari. "Decision combination in multiple classifier systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 1 (1994): 66–75. http://dx.doi.org/10.1109/34.273716.

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27

Wilson, Stewart W. "Classifier systems and the animat problem." Machine Learning 2, no. 3 (November 1987): 199–228. http://dx.doi.org/10.1007/bf00058679.

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28

Robertson, George G., and Rick L. Riolo. "A tale of two classifier systems." Machine Learning 3, no. 2-3 (October 1988): 139–59. http://dx.doi.org/10.1007/bf00113895.

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29

Belew, Richard K., and Stephanie Forrest. "Learning and programming in classifier systems." Machine Learning 3, no. 2-3 (October 1988): 193–223. http://dx.doi.org/10.1007/bf00113897.

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30

Lee, Michael. "The Cognitive Basis of Classifier Systems." Annual Meeting of the Berkeley Linguistics Society 13 (September 10, 1987): 395. http://dx.doi.org/10.3765/bls.v13i0.1822.

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31

Lanzi, Pier Luca. "Learning classifier systems: then and now." Evolutionary Intelligence 1, no. 1 (February 8, 2008): 63–82. http://dx.doi.org/10.1007/s12065-007-0003-3.

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32

Smith, Justin T. H. "Risk neutrality in learning classifier systems." Evolutionary Intelligence 5, no. 2 (May 23, 2012): 69–86. http://dx.doi.org/10.1007/s12065-012-0079-2.

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33

Stanovov, Vladimir, Shakhnaz Akhmedova, and Yukihiro Kamiya. "Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems." Algorithms 13, no. 4 (April 6, 2020): 86. http://dx.doi.org/10.3390/a13040086.

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In this study, a new voting procedure for combining the fuzzy logic based classifiers and other classifiers called confidence-based voting is proposed. This method combines two classifiers, namely the fuzzy classification system, and for the cases when the fuzzy system returns high confidence levels, i.e., the returned membership value is large, the fuzzy system is used to perform classification, otherwise, the second classifier is applied. As a result, most of the sample is classified by the explainable and interpretable fuzzy system, and the second, more accurate, but less interpretable classifier is applied only for the most difficult cases. To show the efficiency of the proposed approach, a set of experiments is performed on test datasets, as well as two problems of estimating the person’s emotional state with the data collected by non-contact vital sensors, which use the Doppler effect. To validate the accuracies of the proposed approach, the statistical tests were used for comparison. The obtained results demonstrate the efficiency of the proposed technique, as it allows for both improving the classification accuracy and explaining the decision making process.
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34

Lin, Jie, and Yan Wang. "Using Composite Classifier Systems to Predict Protein Locations." Applied Mechanics and Materials 195-196 (August 2012): 313–17. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.313.

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Predicting protein location is both an important and challenging topic in molecular and cellular biology. As we all know that the location of proteins sheds light upon the function of a protein whose location was uncertain. But the success of human genome project led to a protein sequence explosion. It is in a great need to develop a computational method for fast and reliably predicting the locations of proteins according to their primary sequences. In this paper, we use composite classifier system that was formed by a set of k-nearest neighbor (K-NN) classifiers, each of which is defined in a different pseudo amino composition vector. The location of a queried protein is determined by the outcome of voting among these constituent individual classifiers. It is show through the outcome that the classifier outperformed single classifier widely used in biological literature.
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35

FENG, XINHUA, XIAOQING DING, YOUSHOU WU, and PATRICK S. P. WANG. "CLASSIFIER COMBINATION AND ITS APPLICATION IN IRIS RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 617–38. http://dx.doi.org/10.1142/s0218001408006314.

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Classifier combination is an effective method to improve the recognition accuracy of a biometric system. It has been applied to many practical biometric systems and achieved excellent performance. However, there is little literature involving theoretical analysis on the effectiveness of classifier combination. In this paper, we investigate classifiers combined with the max and min rules. In particular, we compute the recognition performance of each combined classifier, and illustrate the condition in which the combined classifier outperforms the original unimodal classifier. We focus our study on personal verification, where the input pattern is classified into one of two categories, the genuine or the impostor. For simplicity, we further assume that the matching score produced by the original classifier follows a normal distribution and the outputs of different classifiers are independent and identically distributed. Randomly-generated data are employed to test our conclusion. The influence of finite samples is explored at the same time. Moreover, an iris recognition system, which adopts multiple snapshots to identify a subject, is introduced as a practical application of the above discussions.
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Tama, Bayu Adhi, and Sunghoon Lim. "A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems." Mathematics 8, no. 10 (October 16, 2020): 1814. http://dx.doi.org/10.3390/math8101814.

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Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction.
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Woźniak, Michał, Manuel Graña, and Emilio Corchado. "A survey of multiple classifier systems as hybrid systems." Information Fusion 16 (March 2014): 3–17. http://dx.doi.org/10.1016/j.inffus.2013.04.006.

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38

Cogill-Koez, Dorothea. "Signed language classifier predicates." Sign Language and Linguistics 3, no. 2 (December 31, 2000): 153–207. http://dx.doi.org/10.1075/sll.3.2.03cog.

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It is argued that signed communication systems differ from spoken ones in having not one but two structured systems of representation. In addition to the linguistic mode (which is shared with spoken communication, and which appears to be fundamentally identical across spoken and signed modalities), signers also command distinctive, formal systems of schematic visual representation. These are the forms of signing known as classifier predicates. For the past two decades, signed classifier predicates have been modeled as linguistic. However, the basic formal units of such signing, the combination of these units, and their breakdown, all differ both from patterns seen in other signed forms that have long been recognized as linguistic, and from the classic patterns of language in general. Classifier predicates continue to be modeled as linguistic mostly on the basis of assumptions about alternatives, specifically about the form and acquisition of systems of visual-spatial representation. These assumptions are shown to be incorrect. Signed classifiers are shown to correspond in many respects not merely to visual representation, but to a particular strategy of depiction known as schematic visual representation. This is the mode of depiction that appears to be most natural for both children and adults to master, and that is commonly seen in drawing. There is thus strong evidence that in signed language classifiers we have what, from the point of view of traditional (spoken-language based) linguistics, is a qualitatively new communication mode: formal, structured systems of visual representation that exist side-by-side with linguistic modalities, within the total signed communication system.
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39

GÜNTER, SIMON, and HORST BUNKE. "HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (August 2004): 957–74. http://dx.doi.org/10.1142/s0218001404003496.

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Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.
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40

Panigrahi, Ranjit, Samarjeet Borah, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Moumita Pramanik, Rutvij H. Jhaveri, and Chiranji Lal Chowdhary. "Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research." Mathematics 9, no. 6 (March 23, 2021): 690. http://dx.doi.org/10.3390/math9060690.

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Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.
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41

Nakayama, Mineharu, and Pamela Downing. "Numeral Classifier Systems: The Case of Japanese." Modern Language Journal 81, no. 4 (1997): 578. http://dx.doi.org/10.2307/328920.

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42

Serzisko, Fritz, and Pamela Downing. "Numeral Classifier Systems: The Case of Japanese." Language 74, no. 4 (December 1998): 881. http://dx.doi.org/10.2307/417039.

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43

Jain, L. C., and L. I. Kuncheva. "Designing classifier fusion systems by genetic algorithms." IEEE Transactions on Evolutionary Computation 4, no. 4 (2000): 327–36. http://dx.doi.org/10.1109/4235.887233.

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Westerdale, Thomas H. "Local Reinforcement and Recombination in Classifier Systems." Evolutionary Computation 9, no. 3 (September 2001): 259–81. http://dx.doi.org/10.1162/106365601750405993.

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Abstract:
We investigate classifier systems' reward schemes by way of an example that highlights the interaction of local reward schemes and recombination. We contrast averaging schemes and maximizing schemes. Our example illustrates a sense in which certain recombination operators mesh more gracefully with averaging schemes than with maximizing schemes.
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Holmes, John H., Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson. "Learning classifier systems: New models, successful applications." Information Processing Letters 82, no. 1 (April 2002): 23–30. http://dx.doi.org/10.1016/s0020-0190(01)00283-6.

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Bernstein, Ran, Margarita Osadchy, Daniel Keren, and Assaf Schuster. "LDA classifier monitoring in distributed streaming systems." Journal of Parallel and Distributed Computing 123 (January 2019): 156–67. http://dx.doi.org/10.1016/j.jpdc.2018.09.017.

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Miles, J. C., T. Peggs, and C. J. Moore. "Deriving reservoir operating policies using classifier systems." Civil Engineering and Environmental Systems 19, no. 4 (December 2002): 285–310. http://dx.doi.org/10.1080/10286600215049.

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Booker, Lashon B. "Classifier systems that learn internal world models." Machine Learning 3, no. 2-3 (October 1988): 161–92. http://dx.doi.org/10.1007/bf00113896.

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Ganesan, R., Jin Jionghua, and T. S. Sankar. "A classifier neural network for rotordynamic systems." Mechanical Systems and Signal Processing 9, no. 4 (July 1995): 397–414. http://dx.doi.org/10.1006/mssp.1995.0031.

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Liepins, G. E., M. R. Hilliard, Mark Palmer, and Gita Rangarajan. "Credit assignment and discovery in classifier systems." International Journal of Intelligent Systems 6, no. 1 (January 1991): 55–69. http://dx.doi.org/10.1002/int.4550060104.

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