A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

Comparison of Classifiers for Radar Emitter Type Identification

ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applications such as electronic support measures (ESM). The performance of four ARTMAP variants- ARTMAP (Stage 1), ARTMAP-IC, fuzzy ARTMAP and Gaussian ARTMAP - is assessed with radar data gathered in the field. The k nearest neighbor (kNN) and radial basis function (RDF) classifiers are used for reference. Simulation results indicate that fuzzy ARTMAP and Gaussian ARTMAP achieve an average classification rate consistently higher than that of the other ARTMAP classifers and comparable to that of kNN and RBF. ART-EMAP, ARTMAP-IC and fuzzy ARTMAP require fewer training epochs than Gaussian ARTMAP and RBF, and substantially fewer prototype vectors (thus, smaller physical memory requirements and faster fielded performance) than Gaussian ARTMAP, RBF and kNN. Overall, fuzzy ART MAP performs at least as well as the other classifiers in both accuracy and computational complexity, and better than each of them in at least one of these aspects of performance. Incorporation into fuzzy ARTMAP of the MT- feature of ARTMAP-IC is found to be essential for convergence during on-line training with this data set.Defense Advanced Research Projects Agency and the Office of Naval Research (N000I4-95-1-409 (S.G. and M.A.R.); National Science Foundation (IRI-97-20333) (S.G.); Natural Science and Engineering Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

Nonstimulated early visual areas carry information about surrounding context

Even within the early sensory areas, the majority of the input to any given cortical neuron comes from other cortical neurons. To extend our knowledge of the contextual information that is transmitted by such lateral and feedback connections, we investigated how visually nonstimulated regions in primary visual cortex (V1) and visual area V2 are influenced by the surrounding context. We used functional magnetic resonance imaging (fMRI) and pattern-classification methods to show that the cortical representation of a nonstimulated quarter-field carries information that can discriminate the surrounding visual context. We show further that the activity patterns in these regions are significantly related to those observed with feed-forward stimulation and that these effects are driven primarily by V1. These results thus demonstrate that visual context strongly influences early visual areas even in the absence of differential feed-forward thalamic stimulation

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules

The need to prediscretize numeric attributes before they can be used in association rule learning is a source of inefficiencies in the resulting classifier. This paper describes several new rule tuning steps aiming to recover information lost in the discretization of numeric (quantitative) attributes, and a new rule pruning strategy, which further reduces the size of the classification models. We demonstrate the effectiveness of the proposed methods on postoptimization of models generated by three state-of-the-art association rule classification algorithms: Classification based on Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016), and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from the UCI repository show that the postoptimized models are consistently smaller -- typically by about 50% -- and have better classification performance on most datasets