Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

Not Available

Not AvailableHybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.Not Availabl

Credit scoring using ensemble of various classifiers on reduced feature set

Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model

Gejala Interferensi Pemakaian Bahasa Indonesia Yang Dilakukan Staf Pengajar Di Llngkungan Universitas Airlangga Surabaya

Penelitian ini dilakukan karena mengingat bahwa gejala inter-fel"ensi sel-ing tel"jadi pada semua komponen Pemakaian bahasa IndoneSia yang digunakan oleh staf di Universitas Air-1angga menunjukkan adanya interfel"ensi tel"sebut. bahasa. penl)ajar. geja1a Penelitian ini merumuskan dua permasalahan penting, yaitu (1) Bagaimanakah bentuk-bentuk interferensi mana saja yang berinterferensi da1am bahasa Indonesia, (2) Faktorfaktor yang melatarbelakangi gejala interferensi pada staf pengajar di Univel"sitas Air-langga. Tujuan pene1itian ini adalah (1) mendeskripsikan bentu):-bentuk interfer-ensi dan mengetahui bahasa-bahasa lain yang masuk ke dalam bahasa Indonesia, dan (2) mengetahui beberapa faktor yang menyebabkan gejala interferensi tersebut. Metode yang digunakan di dalam penelitian ini adalah deskriptif kualitatif, yaitu penelitian ydllg dilakukan hanya ber-dasarkan fakta-fakta atau fenomena yang hidup pada penutur-penuturnya. metode ini dapat ditempuh me 1 al ui dua cara, yaitu (a) teknik pengumpulan data, dan (b) teknik analisis data. Teknik pengumpulan data dilakukan dengan menggunakan metode simak atau penyimakan dan metode cakap atau pencakapan, sedangkan teknik analisis data lanl]sung dikenakan pada data-data yang menunjukkan gejala interferensi pemakalan bahasa Indonesia pada staE pengajar eli Universitas Airlangga Hasil penelitian menunjukkan bahwa gejala yang dilakukan staf pengajar di Universitas ada pokok, yaitu leksikal dat-i bahasa Jawa, (2) gramatikal bahasa Jawa, (3) posesif-nya bahasa Jawa, (4) Interferensi 1eksikal dari bahasa, (5) pengaruh terjemahan Which dan WhenJ , (6) pengaruh Is, (7) pengaruh bahasa Indonesia dia1ek disamping Hu, fakto[' yang menyebabkan gejala keakraban, fakto~ prestise dan faktor belakang bahasa. hasil pene1it1an 1n1 disarankan bahwa penutur bahasa lebih memperhatikan kaidah-kaidah bahasa Indonesia demi kepentingan penggunaan bahasa Indonesia yang baik dan bena