Associative Classification Approach Research Articles

A discrete equilibrium optimization algorithm for breast cancer diagnosis

Illness diagnosis is the essential step in designating a treatment. Nowadays, Technological advancements in medical equipment can produce many features to describe breast cancer disease with more comprehensive and discriminant data. Based on the patient’s medical data, several data-driven models are proposed for breast cancer diagnosis using learning techniques such as naive Bayes, neural networks, and SVM. However, the models generated are hardly understandable, so doctors cannot interpret them. This work aims to study breast cancer diagnosis using the associative classification technique. It generates interpretable diagnosis models. In this work, an associative classification approach for breast cancer diagnosis based on the Discrete Equilibrium Optimization Algorithm (DEOA) named Discrete Equilibrium Optimization Algorithm for Associative Classification (DEOA-AC) is proposed. DEOA-AC aims to generate accurate and interpretable diagnosis rules directly from datasets. Firstly, all features in the dataset that contains continuous values are discretized. Secondly, for each class, a new dataset is created from the original dataset and contains only the chosen class’s instances. Finally, the new proposed DEOA is called for each new dataset to generate an optimal rule set. The DEOA-AC approach is evaluated on five well-known and recently used breast cancer datasets and compared with two recently proposed and three classical breast cancer diagnosis algorithms. The comparison results show that the proposed approach can generate more accurate and interpretable diagnosis models for breast cancer than other algorithms.

A novel imbalanced data classification approach for suicidal ideation detection on social media

Suicide has become a serious social health issue in modern society. Suicidal ideation is people’s thoughts about committing or planning suicide. Many factors, such as long-term exposure to negative feelings or life events, can lead to suicidal ideation and suicide attempts. Among these approaches to suicide prevention, early detection of suicidal ideation is one of the most effective ways. Using social networking services provides a platform for people to express their sufferings and feelings in the real world, which provides a source for a deeper investigation into models and approaches for the detection of suicidal intent to enable prevention. This paper addresses the early detection of suicide ideation through the associative classification approach applied to Twitter social media. However, since the number of suicide intention tweets is tiny compared to the number of all the tweets, this leads us to an imbalanced classification problem, in which, the minority class (suicide intention) is more important than the majority class (no suicide intention). In such a situation, classical classifiers usually yield very inaccurate results regarding minor classes, since they can easily discover rules predicting the majority class and overlook those related to the minor. This paper aims to contribute to this line of research by introducing a new interestingness measure to enhance the classification process. This measure highlights the two classes regardless of their imbalanced distribution. Carried out experiments proved that the adapted CBA outweighs in terms of prediction accuracy the original one, and other pioneering baseline classification approaches.

A compact and understandable associative classifier based on overall coverage

Abstract Associative classification is a machine learning approach that aims to build accurate, effective and compact classification models (classifiers) by combining paradigms from classification and association rule mining. Research studies show that associative classification approaches could achieve higher accuracy than some of the traditional classification methods. In this paper, we propose a simple and accurate classification method by selecting “strong” class association rules that highly contribute to improve the overall coverage of the classifier. The advantage of our proposed classifier is that it generates reasonably less rules on bigger datasets compared to traditional rule-based classifiers. We also discuss how the overall coverage of such classifiers affects their classification accuracy. We have performed experiments on 15 real-life datasets from the UCI Machine Learning Database Repository and compared our proposed associative classifier with other 8 well-known classification algorithms on accuracy and the number of classification rules (all differences were tested for statistical significance). Experimental results show that our proposed method was comparative with other well-known classification algorithms on accuracy, it achieved the fourth-highest average accuracy (82.7%) among all classification methods, and tends to outperform the other algorithms in terms of average number of rules (especially on bigger datasets). Although not achieving the best results in terms of classification accuracy, our approach is relatively simple and produces a compact and understandable classifier by exhaustively searching the entire example space.

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

PurposeThe purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.Design/methodology/approachBy employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.FindingsThe results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.Originality/valueConventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

A Distributed Fuzzy Associative Classifier for Big Data.

Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11 000 000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification

Computer-aided diagnosis has gained a significant attention in helping radiologists to improve the accuracy of mammographic detection and diagnostic decision. The aim of proposed research work is to build an efficient and accurate classifier for the classification of mammogram images using a hybrid method by incorporating Genetic Association Rule Miner (GARM) with the learning capability of neural network. A set of features is extracted that comprises of 34 features based on the second and third level of the wavelet decomposition with 13 features measured directly from the gray-level co-occurrence matrix. In order to eliminate the inappropriate features and to increase the efficiency mining process, a multivariate filter is used for feature selection. Based on the selected features, an association rule mining based on modified GARM is used to generate association rules. In the classification phase, the newly generated association rules are used as the input for the creation and training of an artificial neural network. Furthermore, an extended associative classifier using fuzzy feed-forward backpropagation neural network (ACFNN) is proposed as an effective classifier in the context of mammography. The proposed ACFNN performance is compared with associative classifier using feed-forward backpropagation neural network (ACNN). Based on the experimental results, the performance of the proposed ACFNN is improved significantly. Furthermore, it can be inferred that the mammogram classification is better by using ACFNN with accuracy of 95.1 % as compared to ACNN with 93.7 %.

Efficient Mining of Data Streams Using Associative Classification Approach

Data stream associative classification poses many challenges to the data mining community. In this paper, we address four major challenges posed, namely, infinite length, extraction of knowledge with single scan, processing time, and accuracy. Since data streams are infinite in length, it is impractical to store and use all the historical data for training. Mining such streaming data for knowledge acquisition is a unique opportunity and even a tough task. A streaming algorithm must scan data once and extract knowledge. While mining data streams, processing time, and accuracy have become two important aspects. In this paper, we propose PSTMiner which considers the nature of data streams and provides an efficient classifier for predicting the class label of real data streams. It has greater potential when compared with many existing classification techniques. Additionally, we propose a compact novel tree structure called PSTree (Prefix Streaming Tree) for storing data. Extensive experiments conducted on 24 real datasets from UCI repository and synthetic datasets from MOA (Massive Online Analysis) show that PSTMiner is consistent. Empirical results show that performance of PSTMiner is highly competitive in terms of accuracy and performance time when compared with other approaches under windowed streaming model.

An associative rule-based classifier for Arabic medical text

Text classification is one of the methods used for managing, organising and retrieving the needed data among the huge available text. Several methods have been proposed to manipulate the text classification problem. In recent years, some studies proposed the use of Associative Classification (AC) approach. This paper examines an associative classification approach for the categorisation of text typed in Arabic language and related to medical domain. The approach discovers a set of association rules to build a classification model where three steps were applied to build the model: generating association rules, rule ordering and pruning, and then validation. The results of the experiments showed that the ordered decision list approach outperforms other approaches with accuracy reaching 90.6%. In general, the results of the experiments showed that association rule mining is a suitable method for building good classification models to categorise Arabic medical text.

A novel associative classification model based on a fuzzy frequent pattern mining algorithm

Associative classification models are based on two different data mining paradigms, namely pattern classification and association rule mining. These models are very popular for building highly accurate classifiers and have been employed in a number of real world applications.During the last years, several studies and different algorithms have been proposed to integrate associative classification models with the fuzzy set theory, leading to the so-called fuzzy associative classifiers.In this paper, we propose a novel efficient fuzzy associative classification approach based on a fuzzy frequent pattern mining algorithm. Fuzzy items are generated by discretizing the input variables and defining strong fuzzy partitions on the intervals resulting from these discretizations. Then, fuzzy associative classification rules are mined by employing a fuzzy extension of the FP-Growth algorithm, one of the most efficient frequent pattern mining algorithms. Finally, a set of highly accurate classification rules is generated after a pruning stage.We tested our approach on seventeen real-world datasets and compared the achieved results with the ones obtained by using both a non-fuzzy associative classifier, namely CMAR, and two recent state-of-the-art classifiers, namely FARC-HD and D-MOFARC, based on fuzzy association rules. Using non-parametric statistical tests, we show that our approach outperforms CMAR and achieves accuracies similar to FARC-HD and D-MOFARC.

Associative Classification Approaches: Review and Comparison

Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions.

Integrating Noun-Based Feature Ranking and Selection Methods with Arabic Text Associative Classification Approach

Feature ranking and selection (FR&S) is an important preprocessing phase for text classification, and it is in most cases produces small valuable sub-feature space among the whole feature space and reduces the classification errors. As the associative classification (AC) approach is an efficient method and its training and testing depend on the way that features ranked and selected, the examining of feature ranking methods is very significant. This paper presents an integration method of Arabic noun extraction with four FR&S methods: term frequency–inverse document frequency (TF-IDF), document frequency, odd ratio, and class discriminating measure (CDM). Association rule technology uses the result of the integrated feature selection to construct an Arabic text associative classifier. In this study, the majority voting and ordered decision list prediction methods are used by AC to assign test document to its category. A set of experiments are conducted on collection of Arabic text documents, and the experimental results show that our AC method works better with extracted nouns and feature selection method than with feature selection method individually. The AC based on CDM and TF-IDF methods outperforms the other methods in terms of AC accuracy. As the results indicate, the proposed method produces satisfactory classification accuracy and it has good selecting effect on the Arabic text associative classifier.

CLASSIFICATION ON MULTI-LABEL DATASET USING RULE MINING TECHNIQUE

Most recent work has been focused on associative classification technique. Most research work of classification has been done on single label data. But it is not appropriate for some real world application like scene classification, bioinformatics, and text categorization. So that here we proposed multi label classification to solve the issues arise in single label classification. That is very useful in decision making process. Multi-label classification is an extension of single-label classification, and its generality makes it more difficult to solve compare to single label. Also we proposed classification based on association rule mining so that we can accumulate the advantages of both techniques. We can get the benefit of discovering interesting rules from data using rule mining technique and using rule ranking and rule pruning technique we can classified that rules so that redundant rules can be reduced. So that Here proposed work is done on multi label dataset that is classified using rule mining algorithm. Here proposed approach is an accurate and effective multi label classification technique, highly competitive and scalable than other traditional and associative classification approaches.

Principal Association Mining: An efficient classification approach

Classification is one of the key tasks in business intelligence, decision science, and machine learning. Associative classification has aroused significant research interest in recent years due to its superior accuracy. Traditional association rule mining algorithms often yield many redundant and sometimes conflicting class association rules. This paper presents a new, efficient associative classification approach. This new approach produces a compact classifier with a small number of association rules, yet with good classification performance. This approach is based on a novel rule quality metric, named as Principality, which measures an association rule’s classification accuracy and coverage for a specific class. Heuristic methods utilizing the Principality metric are applied to rule pruning and associative classifier construction to produce a compact classifier. This Principal Association Mining (PAM) approach is confirmed to be effective at improving classification accuracy as well as decreasing classifier size by experiments conducted on 17 datasets.

A Fuzzy Associative Classification Approach to Visual Concept Detection

In this paper we present a novel associative classification algorithm for visual concept detection using interest points (color descriptors). Our algorithm relies on fuzzy (soft) partitions to generate a per-descriptor record model which is used for training associative classifiers. In this paper, we use the first 10000 images of the MIR Flickr dataset (with 38 visual concepts — 14 regular annotation topics and 24 pre-annotations) for evaluating our algorithm. Because of its fuzzy nature, our approach also handles polysemy and synonymy (common problems in most crisp (non-fuzzy) image classifiers) very well. As associative classification leverages frequent patterns mined from a given dataset, its performance as adjudged from average area under curve (AUC) values over three-fold cross validation is very good, especially for regular annotation topics and for “scenic” concepts. Our approach has the added advantage that the rules used for classification have clear semantics, and can be comprehended easily, unlike other classifiers, such as SVM, which act as black-boxes. From an empirical perspective (on the MIR Flickr dataset), the performance of our algorithm is much better, than that of either bag-of-words (Linear SVM and SVM RBF Kernel) or Linear SVM based on fuzzy (soft) partitions—both benchmark approaches are based on interest points (color descriptors).

Using Associative Classifiers for Predictive Analysis in Health Care Data Mining

Association rule mining is one of the most important and well researched techniques of data mining for descriptive task, initially used for market basket analysis. It finds all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Classification using Association rule mining is another major Predictive analysis technique that aims to discover a small set of rule in the database that forms an accurate classifier. In this paper, we introduce the combined approach that integrates association rule mining and classification rule mining called Associative Classification (AC). This is new classification approach. The integration is done by focusing on mining a special subset of association rules called classification association rule (CAR). And then classification is being performed using these CAR. Using association rule mining for constructing classification systems is a promising approach. Given the readability of the associative classifiers, they are especially fit to applications were the model may assist domain experts in their decisions. Medical field is a good example was such applications may appear. Consider an example were a physician has to examine a patient. There is a considerable amount of information associated with the patient (e.g. personal data, medical tests, etc.). A classification system can assist the physician in this process. The system can predict if the patient is likely to have a certain disease or present incompatibility with some treatments. Considering the output of the classification model, the physician can make a better decision on the treatment to be applied to this patient. There are many associative classification approaches that have been proposed recently such as CBA, CMAR, CPAR and MCAR and MMAC. Also Combining the Advanced association rule mining with classifiers gives a new type of Associative classifiers with small refinement in the definition of support and confidence that satisfies the validation of downward closure property. We will discuss advanced associative classifiers being proposed in recent years to provide better accuracy as compare to traditional Classifiers.

A New Classification Based on Association Algorithm

Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classification based on Association rule (CBA), which combine disjoint itemsets regardless of their class labels in the training phase, our method joins only itemsets with similar class labels. This saves too many unnecessary itemsets combining during the learning step, and consequently results in massive saving in computational time and memory. Moreover, a new prediction method that utilises multiple rules to make the prediction decision is also developed in this paper. The experimental results on different UCI datasets reveal that LC algorithm outperformed CBA with respect to classification accuracy, memory usage, and execution time on most datasets we consider.

Building an Associative Classifier Based on Fuzzy Association Rules

Classification based on association rules is considered to be effective and advantageous in many cases. However, there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute domains. This paper aims at proposing an associative classification approach, namely Classification with Fuzzy Association Rules (CFAR), where fuzzy logic is used in partitioning the domains. In doing so, the notions of support and confidence are extended, along with the notion of compact set in dealing with rule redundancy and conflict. Furthermore, the corresponding mining algorithm is introduced and tested on benchmarking datasets. The experimental results revealed that CFAR generated better understandability in terms of fewer rules and smother boundaries than the traditional CBA approach while maintaining satisfactory accuracy.

Building an Associative Classifier Based on Fuzzy Association Rules

Classification based on association rules is considered to be effective and advantageous in many cases. However, there is a so-called sharp boundary problem in association rules mining with quantitative attribute domains. This paper aims at proposing an associative classification approach, namely Classification with Fuzzy Association Rules (CFAR), where fuzzy logic is used in partitioning the domains. In doing so, the notions of support and confidence are extended, along with the notion of compact set in dealing with rule redundancy and conflict. Furthermore, the corresponding mining algorithm is introduced and tested on benchmarking datasets. The experimental results revealed that CFAR generated better understandability in terms of fewer rules and smother boundaries than the traditional CBA approach while maintaining satisfactory accuracy.

Mining the data from a hyperheuristic approach using associative classification

Associative classification is a promising classification approach that utilises association rule mining to construct accurate classification models. In this paper, we investigate the potential of associative classifiers as well as other traditional classifiers such as decision trees and rule inducers in solutions (data sets) produced by a general-purpose optimisation heuristic called the hyperheuristic for a personnel scheduling problem. The hyperheuristic requires us to decide which of several simpler search neighbourhoods to apply at each step while constructing a solutions. After experimenting 16 different solution generated by a hyperheuristic called Peckish using different classification approaches, the results indicated that associative classification approach is the most applicable approach to such kind of problems with reference to accuracy. Particularly, associative classification algorithms such as CBA, MCAR and MMAC were able to predict the selection of low-level heuristics from the data sets more accurately than C4.5, RIPPER and PART algorithms, respectively.

Rule Preference Effect in Associative Classification Mining

Classification based on association rule mining, also known as associative classification, is a promising approach in data mining that builds accurate classifiers. In this paper, a rule ranking process within the associative classification approach is investigated. Specifically, two common rule ranking methods in associative classification are compared with reference to their impact on accuracy. We also propose a new rule ranking procedure that adds more tie breaking conditions to the existing methods in order to reduce rule random selection. In particular, our method looks at the class distribution frequency associated with the tied rules and favours those that are associated with the majority class. We compare the impact of the proposed rule ranking method and two other methods presented in associative classification against 14 highly dense classification data sets. Our results indicate the effectiveness of the proposed rule ranking method on the quality of the resulting classifiers for the majority of the benchmark problems, which we consider. This provides evidence that adding more appropriate constraints to break ties between rules positively affects the predictive power of the resulting associative classifiers.