scholarly journals Performance Comparison of Rule Based Classification Algorithms

2011 ◽  
pp. 137-142
Author(s):  
Prafulla Gupta ◽  
Durga Toshniwal
Keyword(s):  
Performance Comparison ◽  
Classification Algorithms ◽  
Rule Generation ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
First Order ◽  
Rule Analysis ◽  
Basic Ideas ◽  
Predictive Rule

Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. CPAR inherits the basic ideas of FOIL (First Order Inductive Learner) algorithm and PRM (Predictive Rule Mining) algorithm in rule generation. It integrates the features of associative classification in predictive rule analysis. In comparison of FOIL, PRM algorithm usually generates more rules. PRM uses concept of lowering weights rather than removing tuple if tuple is satisfied by the rule. The distinction between CPAR and PRM is that instead of choosing only the attribute that displays the best gain on each iteration CPAR may choose a number of attributes if those attributes have gain close to best gain.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

scholarly journals Improving rule-based classification using Harmony Search

2019 ◽  
Vol 5 ◽  
pp. e188 ◽  
Author(s):  
Hesam Hasanpour ◽  
Ramak Ghavamizadeh Meibodi ◽  
Keivan Navi
Keyword(s):  
Harmony Search ◽  
Search Space ◽  
Classification Model ◽  
Classification Algorithms ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Rule Based Classifier ◽  
High Processing ◽  
Fundamental Limitation

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

scholarly journals Efficient Rule Generation for Associative Classification

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 299
Author(s):  
Chartwut Thanajiranthorn ◽  
Panida Songram
Keyword(s):  
Data Representation ◽  
Large Set ◽  
Rule Generation ◽  
Associative Classification ◽  
Rule Mining ◽  
Vertical Data ◽  
Multiple Association ◽  
Unseen Data ◽  
Representation Technique ◽  
Memory Resources

Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).

scholarly journals Prediction of By-Diseases in Diabetic Patients Using Associative Classification with Improved Classifier Accuracy for Decision Support System

2019 ◽  
Vol 8 (6) ◽  
pp. 2625-2628
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Diabetic Patients ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Models ◽  
Associative Classification ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique

Associative Classification in data mining technique formulates more and more simple methods and processes to find and predict the health problems like diabetes, tumors, heart problems, thyroid, cancer, malaria etc. The methods of classification combined with association rule mining gradually helps to predict large amount of data and also builds the accurate classification models for the future analysis. The data in medical area is sometimes vast and containss the information that relates to different diseases. It becomes difficult to estimate and analyze the disease problems that change from period to period based on severity. In this research paper, the use and need of associative classification for the medical data sets and the application of associative classification on the data in order to predict the by-diseases has been put front. The association rules in this context developed in training phase of data have predicted the chance of occurrence of other diseases in persons suffering with diabetes mellitus using Predictive Apriori. The associative classification algorithms like CAR is deployed in the context of accuracy measures.

IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

2021 ◽  
Vol 20 (01) ◽  
pp. 2150010
Author(s):  
Parashu Ram Pal ◽  
Pankaj Pathak ◽  
Shkurte Luma-Osmani
Keyword(s):  
Association Rules ◽  
Search Space ◽  
Classification Rule ◽  
Rule Generation ◽  
Classification Methods ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Selection ◽  
Speed Up ◽  
Experimental Findings

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

Similarity Measure of Breast Cancer Datasets Using Fuzzy Rule-Based Classification by Attribute

2019 ◽  
Vol 1 (1) ◽  
pp. 12-28
Author(s):  
Tengyue Li ◽  
Simon Fong
Keyword(s):  
Breast Cancer ◽  
Information Gain ◽  
Fuzzy Rule ◽  
Ensemble Methods ◽  
Classification Algorithms ◽  
Rule Generation ◽  
Rule Based ◽  
Reasoning System ◽  
Evaluation Approach ◽  
Test Instance

To compare with two datasets based on attributes by using classification algorithms, for the attributes, the authors need to select them by rules and the system is known as rule-based reasoning system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, the authors propose a classifier ensemble-based method for comparison of two breast cancer datasets. The ensemble data mining learning methods are applied to rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two breast cancer datasets. This article introduces a novel fuzzy rule-based classification method called FURIA, to obtain a relationship between two breast cancer datasets. Hence, it can find the similarity between these two datasets. The new method is compared vis-à-vis with other classical statistical approaches such as correlation and mutual information gain.

scholarly journals Improving rule based classification using harmony search

2019 ◽  
Author(s):  
Hesam Hasanpour ◽  
Ramak Ghavamizadeh Meibodi ◽  
Keivan Navi
Keyword(s):  
Harmony Search ◽  
Search Space ◽  
Classification Model ◽  
Classification Algorithms ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Rule Based Classifier ◽  
High Processing ◽  
Fundamental Limitation

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, and etc. Numerous previous studies have shown that this type of classifiers achieves high classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

scholarly journals Improving rule based classification using harmony search

2019 ◽  
Author(s):  
Hesam Hasanpour ◽  
Ramak Ghavamizadeh Meibodi ◽  
Keivan Navi
Keyword(s):  
Harmony Search ◽  
Search Space ◽  
Classification Model ◽  
Classification Algorithms ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Rule Based Classifier ◽  
High Processing ◽  
Fundamental Limitation

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, and etc. Numerous previous studies have shown that this type of classifiers achieves high classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

scholarly journals Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2727
Author(s):  
Hari Prasanth ◽  
Miroslav Caban ◽  
Urs Keller ◽  
Grégoire Courtine ◽  
Auke Ijspeert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gait Analysis ◽  
Real Time ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Clinical Applications ◽  
Performance Comparison ◽  
Heel Strike ◽  
Rule Based ◽  
Pathological Gait

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

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