Analisis dan Implementasi DeEPs: Instance-based Classification Menggunakan Emerging Pattern pada Studi Kasus Teknik Klasifikasi Gejala Fisik Penyakit Demam Tifoid (Tipus)

ABSTRAKSI: Klasifikasi sebagai sebuah task pada data mining bertujuan memprediksi keanggotaan masing-masing instan pada data, untuk memperoleh informasi dari sekumpulan data. Pengklasifikasian data gejala tipus pada tugas akhir ini menggunakan predictive method DeEPs (Decision making by Emerging Patterns). DeEPs merupakan metoda klasifikasi berbasis instan yang artinya setiap record pada data testing akan dibandingkan dengan data training untuk mendapatkan fungsi solusi lokal. Pencarian fungsi solusi lokal ini memanfaatkan konsep Emerging pattern. Emerging pattern marupakan itemset yang memiliki frekuensi berubah secara signifikan antara kelas pada suatu data. Khusus pada metoda klasifikasi DeEPs pola atau itemset yang diamati adalah pola yang relatif muncul hanya pada suatu kelas saja atau dikenal dengan JEP (Jumping Emerging Pattern). Setelah JEP didapatkan maka frekuensi kemunculanya dihitung dan dibandingkan untuk pengelompokan kelas. Implemenstasi metoda DeEPs membutuhkan efesiensi untuk menekan jumlah powerset dari pola yang dicari saat operasi selisih antar maksimum representasi kelas. Hasil klasifikasi DeEPs terhadap data gejala tipus menunjukan performasi yang baik dari segi akurasi dan waktu klasifikasi. Kata Kunci : klasifikasi, predictive method, DeEPs (Decision making by Emerging Pattern), metoda klasifikasi berbasis instan, data testing, data training, fungsi solusi lokal, Emerging pattern, itemset, JEP (Jumping Emerging Pattern).ABSTRACT: Classification as one of data mining task which purpose to predict the membership each instant at data, to gain information. Classification typhus data at this task using predictive method, which is DeEPs (Decision making by Emerging Patterns). DeEPs is an instance-based classification method which mean each record at testing data will be compared with training data to get local solution function. Emenging Pattern concepts are used to get this local solution functions. Emerging Pattern is itemset or pattern where emergence frequency change significantly between classes at data. Particularly at DeEPs classification method the observed pattern are that only appear in a class, known as JEP (Jumping Emerging Pattern). After Jumping Emerging Patterns are founded then these JEPs frequency will be counted and compared to determine the class a record belong. Implementation of DeEPs requires some effeciency to reduce number of powerset of patterns that are searched during difference operation among maximum represention of the class. From experiment which has done DeEPs show good result in terms of accuration and time of classifaction.Keyword: predictive method, DeEPs (Decision making by Emerging Pattern), instance-based classification method, testing data, training data, local solution function, Emerging pattern, itemset, JEP (Jumping Emerging Patter

Analisis dan Implementasi DeEPs: Instance-based Classification Menggunakan Emerging Pattern pada Studi Kasus Teknik Klasifikasi Gejala Fisik Penyakit Demam Tifoid (Tipus)

ABSTRAKSI: Klasifikasi sebagai sebuah task pada data mining bertujuan memprediksi keanggotaan masing-masing instan pada data, untuk memperoleh informasi dari sekumpulan data. Pengklasifikasian data gejala tipus pada tugas akhir ini menggunakan predictive method DeEPs (Decision making by Emerging Patterns). DeEPs merupakan metoda klasifikasi berbasis instan yang artinya setiap record pada data testing akan dibandingkan dengan data training untuk mendapatkan fungsi solusi lokal. Pencarian fungsi solusi lokal ini memanfaatkan konsep Emerging pattern. Emerging pattern marupakan itemset yang memiliki frekuensi berubah secara signifikan antara kelas pada suatu data. Khusus pada metoda klasifikasi DeEPs pola atau itemset yang diamati adalah pola yang relatif muncul hanya pada suatu kelas saja atau dikenal dengan JEP (Jumping Emerging Pattern). Setelah JEP didapatkan maka frekuensi kemunculanya dihitung dan dibandingkan untuk pengelompokan kelas. Implemenstasi metoda DeEPs membutuhkan efesiensi untuk menekan jumlah powerset dari pola yang dicari saat operasi selisih antar maksimum representasi kelas. Hasil klasifikasi DeEPs terhadap data gejala tipus menunjukan performasi yang baik dari segi akurasi dan waktu klasifikasi.Kata Kunci : klasifikasi, predictive method, DeEPs (Decision making by Emerging Pattern), metoda klasifikasi berbasis instan, data testing, data training, fungsi solusi lokal, Emerging pattern, itemset, JEP (Jumping Emerging Pattern).ABSTRACT: Classification as one of data mining task which purpose to predict the membership each instant at data, to gain information. Classification typhus data at this task using predictive method, which is DeEPs (Decision making by Emerging Patterns). DeEPs is an instance-based classification method which mean each record at testing data will be compared with training data to get local solution function. Emenging Pattern concepts are used to get this local solution functions. Emerging Pattern is itemset or pattern where emergence frequency change significantly between classes at data. Particularly at DeEPs classification method the observed pattern are that only appear in a class, known as JEP (Jumping Emerging Pattern). After Jumping Emerging Patterns are founded then these JEPs frequency will be counted and compared to determine the class a record belong. Implementation of DeEPs requires some effeciency to reduce number of powerset of patterns that are searched during difference operation among maximum represention of the class. From experiment which has done DeEPs show good result in terms of accuration and time of classifaction.Keyword: predictive method, DeEPs (Decision making by Emerging Pattern), instance-based classification method, testing data, training data, local solution function, Emerging pattern, itemset, JEP (Jumping Emerging Pattern)

Identification of Interaction Patterns and Classification with Applications to Microarray Data

Emerging patterns represent a class of interaction structures which has been recently proposed as a tool in data mining. In this paper, a new and more general definition refering to underlying probabilities is proposed. The defined interaction patterns carry information about the relevance of combinations of variables for distinguishing between classes. Since they are formally quite similar to the leaves of a classification tree, we propose a fast and simple method which is based on the CART algorithm to find the corresponding empirical patterns in data sets. In simulations, it can be shown that the method is quite effective in identifying patterns. In addition, the detected patterns can be used to define new variables for classification. Thus, we propose a simple scheme to use the patterns to improve the performance of classification procedures. The method may also be seen as a scheme to improve the performance of CARTs concerning the identification of interaction patterns as well as the accuracy of prediction

A Framework to Discover Emerging Patterns for Application in Microarray Data

Various supervised learning and gene selection methods have been used for cancer diagnosis. Most of these methods do not consider interactions between genes, although this might be interesting biologically and improve classification accuracy. Here we introduce a new CART-based method to discover emerging patterns. Emerging patterns are structures of the form (X1>a1)AND(X2<a2) that have differing frequencies in the considered classes. Interaction structures of this kind are of great interest in cancer research. Moreover, they can be used to define new variables for classification. Using simulated data sets, we show that our method allows the identification of emerging patterns with high efficiency. We also perform classification using two publicly available data sets (leukemia and colon cancer). For each data set, the method allows efficient classification as well as the identification of interesting patterns

Multi-Label Super Learner: Multi-Label Classification and Improving Its Performance Using Heterogenous Ensemble Methods

Classification is the task of predicting the label(s) of future instances by learning and inferring from the patterns of instances with known labels. Traditional classification methods focus on single-label classification; however, many real-life problems require multi-label classification that classifies each instance into multiple categories. For example, in sentiment analysis, a person may feel multiple emotions at the same time; in bioinformatics, a gene or protein may have a number of functional expressions; in text categorization, an email, medical record, or social media posting can be identified by various tags simultaneously. As a result of such wide a range of applications, in recent years, multi-label classification has become an emerging research area. There are two general approaches to realize multi-label classification: problem transformation and algorithm adaption. The problem transformation methodology, at its core, converts a multi-label dataset into several single-label datasets, thereby allowing the transformed datasets to be modeled using existing binary or multi-class classification methods. On the other hand, the algorithm adaption methodology transforms single-label classification algorithms in order to be applied to original multi-label datasets. This thesis proposes a new method, called Multi-Label Super Leaner (MLSL), which is a stacking-based heterogeneous ensemble method. An improved multi-label classification algorithm following the problem transformation approach, MLSL combines the prediction power of several multi-label classification methods through an ensemble algorithm, super learner. The performance of this new method is compared to existing problem transformation algorithms, and our numerical results show that MLSL outperforms existing algorithms for almost all of the performance metrics

Income Diversity and the Context of Community Development

The report "Income Diversity and the Context of Community Development" presents the MCIC Income Diversity Index: a three-decade retrospective analysis that seeks to establish a framework to describe patterns of neighborhood economic change in the City of Chicago. This analysis of household income data from the U.S. Census (1970-2000) shows that, while some wealthy Chicago neighborhoods have gotten richer and some poor neighborhoods have gotten poorer, many Chicago neighborhoods are remarkably stable.After researching and developing an innovative, new measure of income diversity, MCIC has identified four distinct patterns of neighborhood economic change in the City of Chicago, since 1970:1) Emerging high net worth2) Emerging low net worth3) Emerging bipolarity4) Stable diversityMCIC identified patterns for each of the 77 Chicago Community Areas to provide an important context for community development strategies.For example, in an Emerging High Income neighborhood (21 in all), the high-income population is increasing and the low-income population is decreasing. Development strategies in these areas should focus on protecting low- to moderate- income households from radical displacement and encourage the use of upgraded public and commercial services.An Emerging Low Income neighborhood, on the other hand, tracks a decline in the high-income population and an increase in the low-income population. In these communities, development efforts should focus on developing infrastructure, investing in buildings and retaining moderate- to high-income households.Additionally, the MCIC study identifies a disturbing "Desertification" trend among half of Chicago's 22 Emerging Low Income communities. In these neighborhoods, disinvestment and neglect have driven away middle- and high- income households.The City's 15 "Bipolar" neighborhoods have seen increases in both high- and low-income residents, and the remaining 19 communities maintain stable, economically diverse populations.Based on household income data from the U.S. Census, the MCIC analysis does not track change in income diversity since the year 2000. However, it does illustrate income trends that provide useful context and baseline data for community development strategists

Interpretable multiclass classification by MDL-based rule lists

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models. In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion. We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion