Testing Market Response to Auditor Change Filings: a comparison of machine learning classifiers

The use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm\u27s auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

SDRS: a new lossless dimensionality reduction for text corpora

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.info:eu-repo/semantics/acceptedVersio

Multi-Model Network Intrusion Detection System Using Distributed Feature Extraction and Supervised Learning

Intrusion Detection Systems (IDSs) monitor network traffic and system activities to identify any unauthorized or malicious behaviors. These systems usually leverage the principles of data science and machine learning to detect any deviations from normalcy by learning from the data associated with normal and abnormal patterns. The IDSs continue to suffer from issues like distributed high-dimensional data, inadequate robustness, slow detection, and high false-positive rates (FPRs). We investigate these challenges, determine suitable strategies, and propose relevant solutions based on the appropriate mathematical and computational concepts. To handle high-dimensional data in a distributed network, we optimize the feature space in a distributed manner using the PCA-based feature extraction method. The experimental results display that the classifiers built upon the features so extracted perform well by giving a similar level of accuracy as given by the ones that use the centrally extracted features. This method also significantly reduces the cumulative time needed for extraction. By utilizing the extracted features, we construct a distributed probabilistic classifier based on Naïve Bayes. Each node counts the local frequencies and passes those on to the central coordinator. The central coordinator accumulates the local frequencies and computes the global frequencies, which are used by the nodes to compute the required prior probabilities to perform classifications. Each node, being evenly trained, is capable of detecting intrusions individually to improve the overall robustness of the system. We also propose a similarity measure-based classification (SMC) technique that works by computing the cosine similarities between the class-specific frequential weights of the values in an observed instance and the average frequency-based data centroid. An instance is classified into the class whose weights for the values in it share the highest level of similarity with the centroid. SMC contributes alongside Naïve Bayes in a multi-model classification approach, which we introduce to reduce the FPR and improve the detection accuracy. This approach utilizes the similarities associated with each class label determined by SMC and the probabilities associated with each class label determined by Naïve Bayes. The similarities and probabilities are aggregated, separately, to form new features that are used to train and validate a tertiary classifier. We demonstrate that such a multi-model approach can attain a higher level of accuracy compared with the single-model classification techniques. The contributions made by this dissertation to enhance the scalability, robustness, and accuracy can help improve the efficacy of IDSs

Sistem Rekomendasi Bacaan Tugas Akhir Jurusan Teknik Informatika Universitas Sriwijaya menggunakan Metode Collaborative Filtering dan Naive Bayes

Sistem Rekomendasi merupakan aplikasi pemberi rekomendasi berupa informasi yang dibutuhkan oleh pengguna berdasarkan feedback dari pengguna lain. Fakultas Ilmu Komputer Universitas Sriwijaya khususnya jurusan Teknik Informatika memerlukan sistem rekomendasi bacaan tugas akhir mengingat sulitnya mengakses data kumpulan tugas akhir yang ada sesuai dengan kategori bidang yang diminati oleh mahasiswa. Untuk mengatasi masalah ini menggunakan metode collaborative filtering dan naive bayes. Metode collaborative filtering digunakan untuk memberi rekomendasi suatu item berupa tugas akhir berdasarkan rate oleh pengguna lainnya, sedangkan naive bayes digunakan untuk mgnklasifikasikan setiap data tugas akhir ke dalam bidang yang sesuai. Berdasarkan hasil percobaan, penelitian ini memiliki akurasi hasil didapatkan 81% dari 100 data uji dan 48 data latih untuk metode naive bayes dan nilai MAE sebesar 2,01 untuk metode collaborative filtering.

Spam Detection Using Machine Learning and Deep Learning

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove these spam messages is important. This dissertation explores the process of text classification from data input to embedded representation of the words in vector form and finally the classification process. Therefore, we have applied different embedding methods to capture both the linguistic and semantic meanings of words. Static embedding methods that are used include Word to Vector (Word2Vec) and Global Vectors (GloVe), while for dynamic embedding the transfer learning of the Bidirectional Encoder Representations from Transformers (BERT) was employed. For classification, both machine learning and deep learning techniques were used to build an efficient and sensitive classification model with good accuracy and low false positive rate. Our result established that the combination of BERT for embedding and machine learning for classification produced better classification results than other combinations. With these results, we developed models that combined the self-feature extraction advantage of deep learning and the effective classification of machine learning. These models were tested on four different datasets, namely: SMS Spam dataset, Ling dataset, Spam Assassin dataset and Enron dataset. BERT+SVC (hybrid model) produced the result with highest accuracy and lowest false positive rate