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

Multi-score Learning for Affect Recognition: the Case of Body Postures

An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets. Unfortunately, inter-rater reliability between observers tends to vary from fair to moderate when dealing with naturalistic expressions. Nevertheless, the most common approach used is to label each expression with the most frequent label assigned by the observers to that expression. In this paper, we propose a general pattern recognition framework that takes into account the variability between observers for automatic affect recognition. This leads to what we term a multi-score learning problem in which a single expression is associated with multiple values representing the scores of each available emotion label. We also propose several performance measurements and pattern recognition methods for this framework, and report the experimental results obtained when testing and comparing these methods on two affective posture datasets

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

Speech Recognition by Composition of Weighted Finite Automata

We present a general framework based on weighted finite automata and weighted finite-state transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including context-dependent units, pronunciation dictionaries, language models and lattices. Furthermore, general but efficient algorithms can used for combining information sources in actual recognizers and for optimizing their application. In particular, a single composition algorithm is used both to combine in advance information sources such as language models and dictionaries, and to combine acoustic observations and information sources dynamically during recognition.Comment: 24 pages, uses psfig.st

I-prune: Item selection for associative classification

Associative classification is characterized by accurate models and high model generation time. Most time is spent in extracting and postprocessing a large set of irrelevant rules, which are eventually pruned.We propose I-prune, an item-pruning approach that selects uninteresting items by means of an interestingness measure and prunes them as soon as they are detected. Thus, the number of extracted rules is reduced and model generation time decreases correspondingly. A wide set of experiments on real and synthetic data sets has been performed to evaluate I-prune and select the appropriate interestingness measure. The experimental results show that I-prune allows a significant reduction in model generation time, while increasing (or at worst preserving) model accuracy. Experimental evaluation also points to the chi-square measure as the most effective interestingness measure for item pruning

A modified multi-class association rule for text mining

Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of rules produced in order to obtain high classification accuracy. This study proposes s a Modified Multi-class Association Rule Mining (mMCAR) that consists of three procedures; rule discovery, rule pruning and group-based class assignment. The rule discovery and rule pruning procedures are designed to reduce the number of classification rules. On the other hand, the group-based class assignment procedure contributes in improving the classification accuracy. Experiments on the structured and unstructured text datasets obtained from the UCI and Reuters repositories are performed in order to evaluate the proposed Association Classifier. The proposed mMCAR classifier is benchmarked against the traditional classifiers and existing Association Classifiers. Experimental results indicate that the proposed Association Classifier, mMCAR, produced high accuracy with a smaller number of classification rules. For the structured dataset, the mMCAR produces an average of 84.24% accuracy as compared to MCAR that obtains 84.23%. Even though the classification accuracy difference is small, the proposed mMCAR uses only 50 rules for the classification while its benchmark method involves 60 rules. On the other hand, mMCAR is at par with MCAR when unstructured dataset is utilized. Both classifiers produce 89% accuracy but mMCAR uses less number of rules for the classification. This study contributes to the text mining domain as automatic classification of huge and widely distributed textual data could facilitate the text representation and retrieval processes

An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome

There have been many studies that depict genotype phenotype relationships by identifying genetic variants associated with a specific disease. Researchers focus more attention on interactions between SNPs that are strongly associated with disease in the absence of main effect. In this context, a number of machine learning and data mining tools are applied to identify the combinations of multi-locus SNPs in higher order data.However, none of the current models can identify useful SNPSNP interactions for high dimensional genome data. Detecting these interactions is challenging due to bio-molecular complexities and computational limitations. The goal of this research was to implement associative classification and study its effectiveness for detecting the epistasis in balanced and imbalanced datasets. The proposed approach was evaluated for two locus epistasis interactions using simulated data. The datasets were generated for 5 different penetrance functions by varying heritability, minor allele frequency and sample size. In total, 23,400 datasets were generated and several experiments are conducted to identify the disease causal SNP interactions. The accuracy of classification by the proposed approach wascompared with the previous approaches. Though associative classification showed only relatively small improvement in accuracy for balanced datasets, it outperformed existing approaches in higher order multi-locus interactions in imbalanced datasets

Discriminative Probabilistic Pattern Mining using Graph for Electronic Health Records

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 김선.전자의료기록(Electronic Health Records)의 임상 노트에는 환자의 병력에 대한 유용한 정보가 많이 포함되어 있다. 그러나 임상 노트는 체계화되지 않은 데이터이며 그 양은 나날이 증가하고 있다. 따라서 임상 노트를 그룹화하고 분류하기 위한 신뢰할 수 있는 데이터 마이닝 기술이 필요하다. 기존의 데이터 마이닝 기술은 키워드의 빈도를 기반으로 생성된 빈발 패턴(frequent patterns)을 이용하여 그룹 분류 작업(classification)을 수행한다. 하지만 이러한 빈발 패턴은 전자의료기록의 임상 노트와 같이 복잡한 데이터의 분류를 위해 필요한 충분히 강력하고 명확하게 구별되는 특징을 갖고 있지 않다. 또한 빈발 패턴 기반 기술은 대규모 전자의료기록 데이터에 적용될 때 확장성과 계산 비용의 문제에 직면한다. 따라서 본 연구에서는 이러한 문제점을 해결하기 위해 확률적 판별 패턴 마이닝(discriminative probabilistic pattern mining) 알고리즘을 소개한다. 확률적 판별 패턴 마이닝 알고리즘에서는 전자의료기록의 임상 노트를 분류하기 위해 그래프 구조를 도입하여 빈발 패턴의 부분 그래프를 생성하게 된다. 본 연구에서는 판별력을 높이기 위해 개별 키워드를 사용하는 대신 이진 특성 조합에서의 동시 출현(co-occurrence)을 사용하여 임상 노트 분류를 위한 빈발 패턴 그래프를 구성한다. 각각의 동시 출현은 판별력(discriminative power)에 따른 log-odds 값으로 그 가중치를 갖는다. 임상 노트의 본질을 반영하는 그래프를 찾기 위해 확률적 판별 부분 그래프 검색을 수행하며 그래프의 허브(hub) 노드에서 시작하여 동적 프로그래밍(dynamic programming)을 사용하여 경로를 찾는다. 이러한 방법으로 검색한 빈발 부분 그래프를 이용하여 전자의료기록의 임상 노트에 대한 분류 작업을 수행하게 된다.Electronic Health Records (EHR) contains plenty of useful information about patients medical history. However, EHR is highly unstructured data and amount of it is growing continuously, that is why there is a need in a reliable data mining technique to group and categorize clinical notes. Although, many existing data mining techniques for group classification use frequent patterns generated based on frequencies of keywords, these patterns do not possess strong enough distinguishing characteristics to show the difference between datasets to classify complex data such as clinical notes in EHR. Also, these techniques encounter scalability and computational cost problems when used on large EHR dataset. To address these issues, we introduce discriminative probabilistic pattern mining algorithm that uses a graph (DPPMG) to generate the subgraphs of frequent patterns for classification in electronic health records. We use co-occurrence, a combination of binary features, which is more discriminative than individual keywords to construct discriminative probabilistic frequent patterns graph for clinical notes classification. Each co-occurrence has a weight of log-odds score that is associated with its discriminative power. The graph, which reflects the essence of clinical notes is searched to find discriminative probabilistic frequent subgraphs. To discover the discriminative frequent subgraphs, we start from a hub node in the graph and use dynamic programming to find a path. The discriminative probabilistic frequent subgraphs discovered by this approach are later used to classify clinical notes of electronic health records.Chapter 1 Introduction and Motivation 1 Chapter 2 Background 4 2.1 Frequent Pattern Based Classification 4 2.2 Discriminative Pattern Mining 5 2.3 Electronic Health Records 6 Chapter 3 Related Work 8 Chapter 4 Overview and Design 10 Chapter 5 Implementation 12 5.1 Dataset 12 5.2 Keyword Extraction and Filtering 15 5.3 Co-occurrence Generation and Graph Construction 16 5.4 Dynamic Programming to Discover Optimal Path 17 Chapter 6 Results and Evaluation 20 6.1 Choosing Starting Hub Node 20 6.2 Qualitative Analysis 22 6.3 Discriminative Power of the Probabilistic Frequent Patterns 24 Chapter 7 Conclusion 26 Bibliography 28 요약 33Maste