Discovering Regression Rules with Ant Colony Optimization

The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible

Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90

Analisis dan Implementasi Algoritma Unordered Rule Sets Ant-Miner Untuk Klasifikasi Pelanggan Potensial Perusahaan Perbankan

ABSTRAKSI: Data mining merupakan sebuah proses untuk mengeksplorasi dan menganalisis data dalam jumlah yang besar dan bertujuan untuk menemukan pola dan keteraturan dari data tersebut yang berguna dalam proses pengambilan keputusan dimasa depan. Data mining sangat cocok untuk memberikan solusi dalam permasalahan data yang besar dalam dunia perbankan. Pada tugas akhir ini dianalisis data-data pelanggan suatu perusahaan perbankan dalam jumlah yang besar dengan menggunakan metode Ant Colony Optimization untuk menemukan pola keteraturan dari data yang akan digunakan sebagai acuan bagi pihak perusahaan untuk mengembangkan perusahaan kedepannya. Pada tugas akhir ini juga diimplementasikan bagaimana pengklasifikasian pelanggan yang potensial dari data mentah dengan menggunakan Algoritma Unordered Rule Sets Ant-Miner yang berguna untuk mengefektifkan pengklasifikasian kaidah-kaidah atau aturan dalam data tersebut. Tujuan kaidah-kaidah ini adalah untuk membangun kasus kedalam satu kelas di luar kelas yang telah didefinisikan sebelumnya berdasarkan dari beberapa atribut prediksi dari kasus tersebut. Algoritma Unordered Rule Sets Ant-Miner merupakan algoritma versi terbaru dari algoritma Ant-Miner yang juga mampu membangkitkan kaidah-kaidah atau aturan dari suatu database (data training) dengan akurasi yang sangat baik pada pengujian terbaiknya. Kata Kunci : data mining, aturan klasifikasi, ant colony optimization (ACO), unordered rule sets ant-miner, pheromone, min_case_per_rule.ABSTRACT: Data mining is a process to explore and analyze data in large quantities and aims to discover patterns and regularities of the data that are useful in decision-making process in the future. Data mining is very suited to provide solutions in large data problems in the banking sector. In this final task analyzed the data of a banking enterprise customers in large numbers using Ant Colony Optimization methods for finding patterns of order that data will be used as a reference for the company to develop the company\u27s future. In this final task is to implement how the classification of potential customers from the raw data using an unordered algorithm Rule Sets Ant-Miner is useful to streamline the classification rules or regulations in the data. The purpose of these rules is to build the case into a class outside the classroom that have been previously defined on the basis of several attributes predictions of the case. Unordered Rule Sets Ant-Miner algorithm is the latest version of the Ant-Miner algorithm is also capable of generating the rules or the rules of a database (training data) with very good accuracy on the best test.Keyword: data mining, classification rule, ant colony optimization (ACO), unordered rule sets ant-miner, pheromone, min_case_per_rule

New Archive-Based Ant Colony Optimization Algorithms for Learning Predictive Rules from Data

Data mining is the process of extracting knowledge and patterns from data. Classification and Regression are among the major data mining tasks, where the goal is to predict a value of an attribute of interest for each data instance, given the values of a set of predictive attributes. Most classification and regression problems involve continuous, ordinal and categorical attributes. Currently Ant Colony Optimization (ACO) algorithms have focused on directly handling categorical attributes only; continuous attributes are transformed using a discretisation procedure in either a preprocessing stage or dynamically during the rule creation. The use of a discretisation procedure has several limitations: (i) it increases the computational runtime, since several candidates values need to evaluated; (ii) requires access to the entire attribute domain, which in some applications all data is not available; (iii) the values used to create discrete intervals are not optimised in combination with the values of other attributes. This thesis investigates the use of solution archive pheromone model, based on Ant Colony Optimization for mixed-variable (ACOMV) algorithm, to directly cope with all attribute types. Firstly, an archive-based ACO classification algorithm is presented, followed by an automatic design framework to generate new configuration of ACO algorithms. Then, we addressed the challenging problem of mining data streams, presenting a new ACO algorithm in combination with a hybrid pheromone model. Finally, the archive-based approach is extended to cope with regression problems. All algorithms presented are compared against well-known algorithms from the literature using publicly available data sets. Our results have been shown to improve the computational time while maintaining a competitive predictive performance

Learning Multi-Tree Classification Models with Ant Colony Optimization

Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain

A new sequential covering strategy for inducing classification rules with ant colony algorithms

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms

Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima